{
"openapi": "3.0.3",
"info": {
"title": "OCR Features",
"version": "2.0",
"description": "Your project description"
},
"paths": {
"/ocr/anonymization_async/": {
"get": {
"operationId": "ocr_anonymization_async_retrieve",
"description": "Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.
\n Please note that a **job status doesn't get updated until a get request** is sent.",
"summary": "Anonymization List Job",
"tags": [
"Anonymization Async"
],
"security": [
{
"FeatureApiAuth": []
},
{}
],
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ListAsyncJobResponse"
},
"examples": {
"ResponseExample": {
"value": {
"jobs": [
{
"providers": "['base64', 'readyredact', 'privateai']",
"nb": 3,
"nb_ok": 3,
"public_id": "c528775e-10af-42d0-a262-5da57464f85c",
"state": "finished",
"created_at": "2026-09-06T03:58:32.403723"
},
{
"providers": "['base64', 'readyredact', 'privateai']",
"nb": 3,
"nb_ok": 3,
"public_id": "2fc0d942-c334-4666-bbe6-d8d92200d4a3",
"state": "finished",
"created_at": "2026-09-06T02:58:32.403735"
},
{
"providers": "['base64', 'readyredact', 'privateai']",
"nb": 3,
"nb_ok": 3,
"public_id": "7000ca9f-e466-404d-a25b-3893fe334de6",
"state": "finished",
"created_at": "2026-09-06T01:58:32.403741"
}
]
},
"summary": "Response Example"
}
}
}
},
"description": ""
}
}
},
"post": {
"operationId": "ocr_anonymization_async_create",
"description": "Available Providers
\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**readyredact**|`v1`|0.05 (per 1 file)|1 file\n|**base64**|`v1`|0.25 (per 1 page)|1 page\n|**privateai**|`v3`|0.01 (per 1 page)|1 page\n\n\nAvailable Providers
\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**base64**|`latest`|0.25 (per 1 page)|1 page\n|**veryfi**|`v8`|0.16 (per 1 request)|1 request\n|**mindee**|`v1`|0.1 (per 1 page)|1 page\n|**extracta**|`v1`|0.1 (per 1 page)|1 page\n\n\n
\n Please note that a **job status doesn't get updated until a get request** is sent.",
"summary": "Custom Document Parsing List Job",
"tags": [
"Custom Document Parsing Async"
],
"security": [
{
"FeatureApiAuth": []
},
{}
],
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ListAsyncJobResponse"
},
"examples": {
"ResponseExample": {
"value": {
"jobs": [
{
"providers": "['extracta', 'amazon']",
"nb": 2,
"nb_ok": 2,
"public_id": "a8d7c813-bcf5-4718-95af-d238dca00198",
"state": "finished",
"created_at": "2026-09-06T03:58:32.131274"
},
{
"providers": "['extracta', 'amazon']",
"nb": 2,
"nb_ok": 2,
"public_id": "f118ac78-7f71-4c7d-8ed2-adce4cf3b3ff",
"state": "finished",
"created_at": "2026-09-06T02:58:32.131285"
},
{
"providers": "['extracta', 'amazon']",
"nb": 2,
"nb_ok": 2,
"public_id": "bf66ba46-0381-4e08-aaf4-2f6ddfc39f66",
"state": "finished",
"created_at": "2026-09-06T01:58:32.131289"
}
]
},
"summary": "Response Example"
}
}
}
},
"description": ""
}
}
},
"post": {
"operationId": "ocr_custom_document_parsing_async_create",
"description": "Available Providers
\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**amazon**|`boto3 1.26.8`|15.0 (per 1000 page)|1 page\n|**extracta**|`v1`|0.1 (per 1 page)|1 page\n\n\nSupported Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**English**|`en`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Italian**|`it`|\n|**Portuguese**|`pt`|\n|**Spanish**|`es`|\n\nAvailable Providers
\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**amazon**|`boto3 (v1.15.18)`|0.05 (per 1 page)|1 page\n|**base64**|`latest`|0.25 (per 1 page)|1 page\n\n\nAvailable Providers
\n\n\n\n|Provider|Model|Version|Document Type|Price|Billing unit|\n|----|----|-------|------|-----|------------|\n|**affinda**|-|`v3`|`invoice`|0.08 (per 1 page)|1 page\n|**affinda**|-|`v3`|`receipt`|0.07 (per 1 page)|1 page\n|**amazon**|-|`boto3 1.26.8`|-|0.01 (per 1 page)|1 page\n|**base64**|-|`latest`|-|0.25 (per 1 page)|1 page\n|**google**|-|`DocumentAI v1 beta3`|`invoice`|0.01 (per 1 page)|10 page\n|**google**|-|`DocumentAI v1 beta3`|`receipt`|0.01 (per 1 page)|10 page\n|**klippa**|-|`v1`|-|0.1 (per 1 file)|1 file\n|**microsoft**|-|`rest API 4.0 (2024-02-29-preview)`|-|0.01 (per 1 page)|1 page\n|**mindee**|-|`v1.2`|-|0.1 (per 1 page)|1 page\n|**tabscanner**|-|`latest`|-|0.08 (per 1 page)|1 page\n|**veryfi**|-|`v8`|`receipt`|0.08 (per 1 file)|1 file\n|**veryfi**|-|`v8`|`invoice`|0.16 (per 1 file)|1 file\n|**eagledoc**|-|`v1`|-|0.03 (per 1 page)|1 page\n|**extracta**|-|`v1`|-|0.1 (per 1 page)|1 page\n|**openai**|**gpt-4o**|`v1.0`|-|0.04 (per 1 page)|1 page\n\n\nSupported Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Amharic**|`am`|\n|**Arabic**|`ar`|\n|**Armenian**|`hy`|\n|**Azerbaijani**|`az`|\n|**Basque**|`eu`|\n|**Belarusian**|`be`|\n|**Bengali**|`bn`|\n|**Bosnian**|`bs`|\n|**Bulgarian**|`bg`|\n|**Burmese**|`my`|\n|**Catalan**|`ca`|\n|**Cebuano**|`ceb`|\n|**Chinese**|`zh`|\n|**Corsican**|`co`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Esperanto**|`eo`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Galician**|`gl`|\n|**Georgian**|`ka`|\n|**German**|`de`|\n|**Gujarati**|`gu`|\n|**Haitian**|`ht`|\n|**Hausa**|`ha`|\n|**Hawaiian**|`haw`|\n|**Hebrew**|`he`|\n|**Hindi**|`hi`|\n|**Hmong**|`hmn`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Igbo**|`ig`|\n|**Indonesian**|`id`|\n|**Irish**|`ga`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Javanese**|`jv`|\n|**Kannada**|`kn`|\n|**Kazakh**|`kk`|\n|**Khmer**|`km`|\n|**Kinyarwanda**|`rw`|\n|**Kirghiz**|`ky`|\n|**Korean**|`ko`|\n|**Kurdish**|`ku`|\n|**Lao**|`lo`|\n|**Latin**|`la`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Luxembourgish**|`lb`|\n|**Macedonian**|`mk`|\n|**Malagasy**|`mg`|\n|**Malay (macrolanguage)**|`ms`|\n|**Malayalam**|`ml`|\n|**Maltese**|`mt`|\n|**Maori**|`mi`|\n|**Marathi**|`mr`|\n|**Modern Greek (1453-)**|`el`|\n|**Mongolian**|`mn`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Norwegian**|`no`|\n|**Nyanja**|`ny`|\n|**Oriya (macrolanguage)**|`or`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Pushto**|`ps`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Samoan**|`sm`|\n|**Scottish Gaelic**|`gd`|\n|**Serbian**|`sr`|\n|**Shona**|`sn`|\n|**Sindhi**|`sd`|\n|**Sinhala**|`si`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Somali**|`so`|\n|**Southern Sotho**|`st`|\n|**Spanish**|`es`|\n|**Sundanese**|`su`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tagalog**|`tl`|\n|**Tajik**|`tg`|\n|**Tamil**|`ta`|\n|**Tatar**|`tt`|\n|**Telugu**|`te`|\n|**Thai**|`th`|\n|**Turkish**|`tr`|\n|**Turkmen**|`tk`|\n|**Uighur**|`ug`|\n|**Ukrainian**|`uk`|\n|**Urdu**|`ur`|\n|**Uzbek**|`uz`|\n|**Vietnamese**|`vi`|\n|**Welsh**|`cy`|\n|**Western Frisian**|`fy`|\n|**Xhosa**|`xh`|\n|**Yiddish**|`yi`|\n|**Yoruba**|`yo`|\n|**Zulu**|`zu`|\n\nSupported Detailed Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Catalan (Spain)**|`ca-ES`|\n|**Chinese (China)**|`zh-CN`|\n|**Chinese (China)**|`zh-cn`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Chinese (Taiwan)**|`zh-tw`|\n|**Danish (Denmark)**|`da-DK`|\n|**Dutch (Netherlands)**|`nl-NL`|\n|**English (United Kingdom)**|`en-GB`|\n|**English (United States)**|`en-US`|\n|**French (Canada)**|`fr-CA`|\n|**French (France)**|`fr-FR`|\n|**French (Switzerland)**|`fr-CH`|\n|**German (Germany)**|`de-DE`|\n|**German (Switzerland)**|`de-CH`|\n|**Italian (Italy)**|`it-IT`|\n|**Italian (Switzerland)**|`it-CH`|\n|**Portuguese (Portugal)**|`pt-PT`|\n|**Spanish (Spain)**|`es-ES`|\n\nSupported Models
\n\nDefault Models
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n\nAvailable Providers
\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**amazon**|-|`boto3 (v1.15.18)`|0.025 (per 1 page)|1 page\n|**base64**|-|`latest`|0.2 (per 1 page)|1 page\n|**microsoft**|-|`rest API 4.0 (2024-02-29-preview)`|0.01 (per 1 page)|1 page\n|**mindee**|-|`v2`|0.1 (per 1 page)|1 page\n|**klippa**|-|`v1`|0.1 (per 1 file)|1 file\n|**affinda**|-|`v3`|0.07 (per 1 file)|1 file\n|**openai**|**gpt-4o**|`v1`|0.02 (per 1 page)|1 page\n\n\nSupported Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Arabic**|`ar`|\n|**Bengali**|`bn`|\n|**Bulgarian**|`bg`|\n|**Chinese**|`zh`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Gujarati**|`gu`|\n|**Hebrew**|`he`|\n|**Hindi**|`hi`|\n|**Hungarian**|`hu`|\n|**Indonesian**|`id`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Kannada**|`kn`|\n|**Korean**|`ko`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Macedonian**|`mk`|\n|**Malayalam**|`ml`|\n|**Marathi**|`mr`|\n|**Modern Greek (1453-)**|`el`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Norwegian**|`no`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Somali**|`so`|\n|**Spanish**|`es`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tagalog**|`tl`|\n|**Tamil**|`ta`|\n|**Telugu**|`te`|\n|**Thai**|`th`|\n|**Turkish**|`tr`|\n|**Ukrainian**|`uk`|\n|**Urdu**|`ur`|\n|**Vietnamese**|`vi`|\n\nSupported Detailed Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (China)**|`zh-cn`|\n|**Chinese (Taiwan)**|`zh-tw`|\n|**English (United States)**|`en-US`|\n|**French (France)**|`fr-FR`|\n|**German (Germany)**|`de-DE`|\n|**Italian (Italy)**|`it-IT`|\n|**Portuguese (Portugal)**|`pt-PT`|\n|**Spanish (Spain)**|`es-ES`|\n\nSupported Models
\n\nDefault Models
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n\nSupported Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Abaza**|`abq`|\n|**Adyghe**|`ady`|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Angika**|`anp`|\n|**Arabic**|`ar`|\n|**Assamese**|`as`|\n|**Asturian**|`ast`|\n|**Avaric**|`av`|\n|**Awadhi**|`awa`|\n|**Azerbaijani**|`az`|\n|**Bagheli**|`bfy`|\n|**Basque**|`eu`|\n|**Belarusian**|`be`|\n|**Bengali**|`bn`|\n|**Bhojpuri**|`bho`|\n|**Bihari languages**|`bh`|\n|**Bislama**|`bi`|\n|**Bodo (India)**|`brx`|\n|**Bosnian**|`bs`|\n|**Braj**|`bra`|\n|**Breton**|`br`|\n|**Bulgarian**|`bg`|\n|**Bundeli**|`bns`|\n|**Buriat**|`bua`|\n|**Camling**|`rab`|\n|**Catalan**|`ca`|\n|**Cebuano**|`ceb`|\n|**Chamorro**|`ch`|\n|**Chechen**|`ce`|\n|**Chhattisgarhi**|`hne`|\n|**Chinese**|`zh`|\n|**Cornish**|`kw`|\n|**Corsican**|`co`|\n|**Crimean Tatar**|`crh`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dargwa**|`dar`|\n|**Dari**|`prs`|\n|**Dhimal**|`dhi`|\n|**Dogri (macrolanguage)**|`doi`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Erzya**|`myv`|\n|**Estonian**|`et`|\n|**Faroese**|`fo`|\n|**Fijian**|`fj`|\n|**Filipino**|`fil`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Friulian**|`fur`|\n|**Gagauz**|`gag`|\n|**Galician**|`gl`|\n|**German**|`de`|\n|**Gilbertese**|`gil`|\n|**Goan Konkani**|`gom`|\n|**Gondi**|`gon`|\n|**Gurung**|`gvr`|\n|**Haitian**|`ht`|\n|**Halbi**|`hlb`|\n|**Hani**|`hni`|\n|**Haryanvi**|`bgc`|\n|**Hawaiian**|`haw`|\n|**Hindi**|`hi`|\n|**Hmong Daw**|`mww`|\n|**Ho**|`hoc`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Inari Sami**|`smn`|\n|**Indonesian**|`id`|\n|**Ingush**|`inh`|\n|**Interlingua (International Auxiliary Language Association)**|`ia`|\n|**Inuktitut**|`iu`|\n|**Irish**|`ga`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Jaunsari**|`jns`|\n|**Javanese**|`jv`|\n|**K'iche'**|`quc`|\n|**Kabardian**|`kbd`|\n|**Kabuverdianu**|`kea`|\n|**Kachin**|`kac`|\n|**Kalaallisut**|`kl`|\n|**Kangri**|`xnr`|\n|**Kara-Kalpak**|`kaa`|\n|**Karachay-Balkar**|`krc`|\n|**Kashubian**|`csb`|\n|**Kazakh**|`kk`|\n|**Khaling**|`klr`|\n|**Khasi**|`kha`|\n|**Kirghiz**|`ky`|\n|**Korean**|`ko`|\n|**Korku**|`kfq`|\n|**Koryak**|`kpy`|\n|**Kosraean**|`kos`|\n|**Kumarbhag Paharia**|`kmj`|\n|**Kumyk**|`kum`|\n|**Kurdish**|`ku`|\n|**Kurukh**|`kru`|\n|**Kölsch**|`ksh`|\n|**Lak**|`lbe`|\n|**Lakota**|`lkt`|\n|**Latin**|`la`|\n|**Latvian**|`lv`|\n|**Lezghian**|`lez`|\n|**Lithuanian**|`lt`|\n|**Lower Sorbian**|`dsb`|\n|**Lule Sami**|`smj`|\n|**Luxembourgish**|`lb`|\n|**Mahasu Pahari**|`bfz`|\n|**Maithili**|`mai`|\n|**Malay (macrolanguage)**|`ms`|\n|**Maltese**|`mt`|\n|**Manx**|`gv`|\n|**Maori**|`mi`|\n|**Marathi**|`mr`|\n|**Marshallese**|`mh`|\n|**Mongolian**|`mn`|\n|**Montenegrin**|`cnr`|\n|**Neapolitan**|`nap`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Newari**|`new`|\n|**Niuean**|`niu`|\n|**Nogai**|`nog`|\n|**Northern Sami**|`se`|\n|**Norwegian**|`no`|\n|**Occitan (post 1500)**|`oc`|\n|**Old English (ca. 450-1100)**|`ang`|\n|**Ossetian**|`os`|\n|**Pali**|`pi`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Pushto**|`ps`|\n|**Romanian**|`ro`|\n|**Romansh**|`rm`|\n|**Russian**|`ru`|\n|**Sadri**|`sck`|\n|**Samoan**|`sm`|\n|**Sanskrit**|`sa`|\n|**Santali**|`sat`|\n|**Scots**|`sco`|\n|**Scottish Gaelic**|`gd`|\n|**Serbian**|`sr`|\n|**Sherpa**|`xsr`|\n|**Sirmauri**|`srx`|\n|**Skolt Sami**|`sms`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Somali**|`so`|\n|**Southern Sami**|`sma`|\n|**Spanish**|`es`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tabassaran**|`tab`|\n|**Tagalog**|`tl`|\n|**Tajik**|`tg`|\n|**Tatar**|`tt`|\n|**Tetum**|`tet`|\n|**Thangmi**|`thf`|\n|**Tonga (Tonga Islands)**|`to`|\n|**Turkish**|`tr`|\n|**Turkmen**|`tk`|\n|**Tuvinian**|`tyv`|\n|**Uighur**|`ug`|\n|**Ukrainian**|`uk`|\n|**Upper Sorbian**|`hsb`|\n|**Urdu**|`ur`|\n|**Uzbek**|`uz`|\n|**Vietnamese**|`vi`|\n|**Volapük**|`vo`|\n|**Walser**|`wae`|\n|**Welsh**|`cy`|\n|**Western Frisian**|`fy`|\n|**Yucateco**|`yua`|\n|**Zhuang**|`za`|\n|**Zulu**|`zu`|\n\nSupported Detailed Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Arabic (Pseudo-Accents)**|`ar-XA`|\n|**Belarusian**|`be-cyrl`|\n|**Belarusian (Latin)**|`be-latn`|\n|**Chinese (China)**|`zh-CN`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Chinese (Traditional)**|`zh-Hant`|\n|**Danish (Denmark)**|`da-DK`|\n|**Dutch (Netherlands)**|`nl-NL`|\n|**English (United States)**|`en-US`|\n|**Finnish (Finland)**|`fi-FI`|\n|**French (France)**|`fr-FR`|\n|**German (Germany)**|`de-DE`|\n|**Hungarian (Hungary)**|`hu-HU`|\n|**Italian (Italy)**|`it-IT`|\n|**Japanese (Japan)**|`ja-JP`|\n|**Kara-Kalpak (Cyrillic)**|`kaa-Cyrl`|\n|**Kazakh**|`kk-cyrl`|\n|**Kazakh (Latin)**|`kk-latn`|\n|**Korean (South Korea)**|`ko-KR`|\n|**Kurdish (Arabic)**|`ku-arab`|\n|**Kurdish (Latin)**|`ku-latn`|\n|**Polish**|`pl-PO`|\n|**Portuguese (Portugal)**|`pt-PT`|\n|**Region: Czechia**|`cz-CZ`|\n|**Region: Greece**|`gr-GR`|\n|**Russian (Russia)**|`ru-RU`|\n|**Serbian (Cyrillic, Montenegro)**|`sr-Cyrl-ME`|\n|**Serbian (Latin)**|`sr-latn`|\n|**Serbian (Latin, Montenegro)**|`sr-Latn-ME`|\n|**Spanish (Spain)**|`es-ES`|\n|**Swedish (Sweden)**|`sv-SE`|\n|**Turkish (Türkiye)**|`tr-TR`|\n|**Uzbek (Arabic)**|`uz-arab`|\n|**Uzbek (Cyrillic)**|`uz-cyrl`|\n\n
\n Please note that a **job status doesn't get updated until a get request** is sent.",
"summary": "Ocr Async List Job",
"tags": [
"Ocr Async"
],
"security": [
{
"FeatureApiAuth": []
},
{}
],
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ListAsyncJobResponse"
},
"examples": {
"ResponseExample": {
"value": {
"jobs": [
{
"providers": "['microsoft', 'mistral', 'amazon']",
"nb": 3,
"nb_ok": 3,
"public_id": "adcbfd02-8ac8-450f-bab5-cd846b0f3f89",
"state": "finished",
"created_at": "2026-09-06T03:58:32.209981"
},
{
"providers": "['microsoft', 'mistral', 'amazon']",
"nb": 3,
"nb_ok": 3,
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${ }^{1}$ Department of Computer Engineering
L D College Of Engineering, Gujarat Technological University, Gujarat, India\n\n\n#### Abstract\n\nThis paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years. Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).\n\n\nKeywords: OCR, online, offline, online, zoning, euler number.\n\n## 1. Introduction\n\nOCR is an approach that provides a full alphanumeric recognition of printed or handwritten characters at electronically by simply scanning them and generating into a form that can be scanned through a scanner and then the recognition engine of the OCR system interpret the images and turn images of handwritten or printed characters into ASCII data (machine-readable characters).Character recognition also popularly referred as optical character recognition (OCR) is a field of research that has immense potential in future where we want to track and locate every piece of information being exchanged. The problem with the hand written text is due to uncertainties such as variation in calligraphy over period of time, similarity in text, variation in styles of writing [3] The character recognition system helps in making the communication between a human and a computer easy.[4] The character recognition is basically classified into two types: offline handwritten text recognition, online handwritten text recognition. Offline means the text written on the plain paper or sheet and then the writing is usually captured optically by a scanner and the completed writing is available as an image. Online means the text written on any digital devices such as tablets using stylus i.e. the two dimensional coordinates of successive points are represented as a function of time and the order of strokes made by the writer are also available.[6]\n\n## 2. Applications of optical character recognition\n\nThe area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification, document reading, mail sorting, signature verification, writer identification., license plate recognition system, smart card processing system, automatic data entry, bank cheque /DD processing, money counting machine, postal automation, address and zip code recognition etc many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency $[2,4,6,7]$.\n\n## 3. Potential problem areas for OCR\n\n1. The same characters differ in sizes, shapes and styles from person to person and even from time to time with the same person. The source of confusion is the high level of abstraction: there are thousands styles of type in common use plus variations in calligraphy and a character recognition program must recognize most of these.\n2. Like any image, visual characters are subject to spoilage due to noise. Some images containing characters are already blurred or not clear which makes them difficult to process. Noise consists of random changes to a pattern, particularly near the edges. A character with much noise may be interpreted as a completely different character by a computer program.\n3. There are no hard-and-fast rules that define the appearance of a visual character. Hence rules need to be heuristically deduced from the samples.\n\n## 4. Phases of OCR\n\n| Data Acquisition |\n| :--: |\n| Pre processing |\n| Segmentation |\n| Normalization |\n| Feature Extraction |\n| Classification |\n| Post Processing |## 1. Data Acquisition\n\nMost Important initial phase in OCR is to gather the image from either device sensor like PDA or tablets in case on online recognition or getting the images containing characters directly for offline recognition.\n\nIn Image acquisition, the recognition system acquires a scanned image as an input image. The image should have a specific format such as JPEG, BMP etc. This image is acquired through a scanner, digital camera or any other suitable digital input device. Data samples for the experiment have been collected from different individuals [9].\n\n## 2. Pre Processing\n\nThe goal of pre-processing is to simplify the pattern recognition problem without missing any vital information. It reduces the noises and inconsistent data. It enhances the image and prepares it for the next steps [3].\n\nPreprocessing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Therefore, the main task in preprocessing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, preprocessing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, preprocessing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages [2].\n\nPreprocessing can be done through various ways Binarization, Noise reduction, Stroke width normalization, Skew correction, Slant removal, Filtering, Morphological Operations, Noise Modelling, Skew Normalization, Size Normalization, Contour Smoothing, Compression, Thresholding, Thinning etc\n\n\nFigure 1: Slant Removal\n\n\nFigure 2: Normalization of ' e ' and ' 1 ' as in [9]\n\n## 3. Segmentation\n\nSegmentation is an integral part of any text based recognition system. It assures efficiency of classification and\nrecognition. Accuracy of character recognition heavily depends upon segmentation phase.\n\n\nFigure 3: Segmentation [9]\n\n## 4. Normalization\n\nThe results of segmentation process provides isolated characters which are ready to pass through feature extraction stage, thus the isolated characters are reduced to a specific size depending on the methods used. The segmentation process essentially renders the image in the form of $\\mathrm{m} * \\mathrm{n}$ matrix. These matrices are then generally normalized by reducing the size and removing the redundant information from the image without losing any important information.\n\n## 5. Feature Extraction\n\nFeature extraction is the process of extracting the relevant features from objects/alphabets to form a feature vectors. These feature vectors is then used by classifiers to recognize the input unit with target output unit. It becomes easier for the classifier to classify between different classes by looking at these features as it allows fairly easy to distinguish.\n\nFeature extraction is also defined as extracting the raw data the information which is most relevant for classification purposes in the sense of minimizing the pattern variability.[1]\nDue to the nature of handwriting with its high degree of variability and imprecision obtaining these features, is a difficult task. Feature extraction methods are based on 3 types of features:\n\n- Statistical\n- Structural\n- Global transformations and moments\n\nStatistical Features includes:\n\n## 1. Zoning\n\nThe character image is divided into NxM zones. From each zone features are extracted to form the feature vector. Thegoal of zoning is to obtain the local characteristics instead of global characteristics.\n\n\nFigure 4: zoning\nAfter dividing the character into different zones you can compare the density or direction features of it and classify each one of them.\n\n## 2. Projection Histograms\n\nThe basic idea behind using projections is that character images, which are 2-D signals, can be represented as 1D signal. These features, although independent to noise and deformation, depend on rotation. Projection histograms count the number of pixels in each column and row of a character image. Projection histograms can separate characters such as \"m\" and \"n\".\n\n\nFigure 5: Projection Histogram\n\n## 3. Profiles\n\nThe profile counts the number of pixels (distance) between the bounding box of the character image and the edge of the character. The profiles describe well the external shapes of characters and allow distinguishing between a great number of letters, such as \"p\" and \"q\".\n\n\nFigure 6: Profiling\n\n## 4. Structural features:\n\nStructural features are based on topological and geometrical properties of the character, such as aspect ratio, cross points, loops, branch points, strokes and their directions, inflection between two points, horizontal curves at top or bottom, etc.\n\nGlobal Transformations-Moments:\nThe Fourier Transform (FT) of the contour of the image is calculated. Since the first $n$ coefficients of the FT can be used in order to reconstruct the contour, then these $n$ coefficients are considered to be a $n$-dimensional feature vector that represents the character.\n\n\nFigure 7: Contouring\n\n## 6 Classification\n\nThe results Classification is the last stage where we train the neural net using the feature vectors obtained during feature extraction method against the required targets. To optimize the whole recognition process, several combination methods of multilayer perceptron have been devised. E.g.: k-Nearest Neighbour (k-NN), Bayes Classifier, Neural Networks (NN), Hidden Markov Models (HMM), Support Vector Machines (SVM), etc there is no such thing as the \"best classifier\". The use of classifier depends on many factors, such as available training set, number of free parameters etc.\n\n## 7. Post Processing\n\nThe goal of post processing is the incorporation of context and shape information in all the stages of OCR systems is necessary for meaningful improvements in recognition rates.\n\n## 5. Conclusion\n\nThe character recognition methods have been introduced and developed over the years. In this paper, I have tried to explain the overview of the whole OCR process and the methods related to it. Many researchers try to hybrid two or more different methods and compare the results for efficiency but again this will be application specific and parameter specific. OCR has been implemented in various countries for recognizing different languages as well.\n\n## References\n\n[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, \"Feature Extraction Methods for Character Recognition-A Survey\", July 1995\n[2] Yasser Alginahi, Taibah University Kingdom of Saudi Arabia, \"Preprocessing Techniques in Character Recognition\"\n[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, Benoy Kumar Thakur, \"Recent Trends and Tools for Feature Extraction\n[4] in OCR Technology\", International Journal of Soft Computing and Engineering (IJSCE)\n[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok M. Sapkal, \"Survey of Methods for Character Recognition\", International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012\n[7] Mohanad Alata, Mohammad Al-Shabi \"TEXT DETECTION AND CHARACTER\n[8] RECOGNITION USING FUZZY IMAGE PROCESSING\", Journal of ELECTRICAL ENGINEERING, VOL. 57, NO. 5, 2006, 258-267\n[9] Rejean Plamondon, Fellow, IEEE and Sargur N. Shrihari, Fellow, IEEE, \"On-line and Off-line Handwriting Recognition: A comprehensive Survey\", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL 22, NO. 1 JANUARY 2000\n[10]Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, \"An Improved Zone Based Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using Euler Number\", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012\n[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, \"Neural Network Based Recognition System Integrating Feature\n\nExtraction and Classification for English Handwritten\", IJE TRANSACTIONS B: Applications Vol. 25, No. 2, (May 2012) 99-106\n[12] Nafiz Arica and Fatos T. Yarman-Vural, \"An Overview of Character Recognition Focused on\n[13]Off-Line Handwriting\", IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 31, NO. 2, MAY 2001\n\n## Author Profile\n\nUmal Patel, pursuing her Master Degree in Computer Science \\& Technology from Gujarat Technological University (L D College of Eng., Ahmedabad), received her Bachelor Degree in Computer Engg. from Gujarat University in 2008. Presently working as Assistant Professor in Department of MCA, L.J Institute of Technology. Earlier he has served as Software Test Engineer in Lodestone Software Services since June 2008. Her area of interest is Compilers and Image Processing.",
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"raw_text": "International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064\nAn Introduction to the Process of Optical Character\nRecognition\nUmal Patel1\n1\nDepartment of Computer Engineering\nLD College Of Engineering, Gujarat Technological University, Gujarat, India\nAbstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification\nof the alphabets and numbers of English language. Character recognition has long been a essential area for research since years.\nRecognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely\ndifficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for\nresearchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).\nKeywords: OCR, online, offline, online, zoning, euler number.\n1. Introduction\n3. Potential problem areas for OCR\nOCR is an approach that provides a full alphanumeric\n1. The same characters differ in sizes, shapes and styles\nrecognition of printed or handwritten characters at\nfrom person to person and even from time to time with\nelectronically by simply scanning them and generating into a\nthe same person. The source of confusion is the high\nform that can be scanned through a scanner and then the\nlevel of abstraction: there are thousands styles of type in\nrecognition engine of the OCR system interpret the images\ncommon use plus variations in calligraphy and a\nand turn images of handwritten or printed characters into\ncharacter recognition program must recognize most of\nASCII data (machine-readable characters). Character\nthese.\nrecognition also popularly referred as optical character\n2. Like any image, visual characters are subject to spoilage\nrecognition (OCR) is a field of research that has immense\ndue to noise. Some images containing characters are\npotential in future where we want to track and locate every\nalready blurred or not clear which makes them difficult to\npiece of information being exchanged. The problem with the\nprocess. Noise consists of random changes to a pattern,\nhand written text is due to uncertainties such as variation in\nparticularly near the edges. A character with much noise\ncalligraphy over period of time, similarity in text, variation\nmay be interpreted as a completely different character by\nin styles of writing [3] The character recognition system\na computer program.\nhelps in making the communication between a human and a\n3. There are no hard-and-fast rules that define the\ncomputer easy.[4] The character recognition is basically\nappearance of a visual character. Hence rules need to be\nclassified into two types: offline handwritten text\nheuristically deduced from the samples.\nrecognition, online handwritten text recognition. Offline\nmeans the text written on the plain paper or sheet and then\n4. Phases of OCR\nthe writing is usually captured optically by a scanner and the\ncompleted writing is available as an image. Online means the\nData Acquisition\ntext written on any digital devices such as tablets using\nstylus i.e. the two dimensional coordinates of successive\npoints are represented as a function of time and the order of\nPre processing\nstrokes made by the writer are also available.[6]\nSegmentation\n2. Applications\nof\noptical\ncharacter\nrecognition\nNormalization\nThe area of OCR is becoming an integral part of document\nFeature Extraction\nscanners, and is used in many applications such as postal\nprocessing, script recognition, banking, security (i.e.\nClassification\npassport authentication) and language identification,\ndocument reading, mail sorting, signature verification, writer\nidentification., license plate recognition system, smart card\nPost Processing\nprocessing system, automatic data entry, bank cheque /DD\nprocessing, money counting machine, postal automation,\naddress and zip code recognition etc many organizations are\ndepending on OCR systems to eliminate the human\ninteractions for better performance and efficiency [2,4,6,7].\nVolume 2 Issue 5, May 2013\nwww.ijsr.net\n155\nInternational Journal of Science and Research (IJSR), India Online ISSN: 2319-7064\n1. Data Acquisition\nrecognition. Accuracy of character recognition heavily\ndepends upon segmentation phase.\nMost Important initial phase in OCR is to gather the image\nfrom either device sensor like PDA or tablets in case on\nonline recognition or getting the images containing\ncharacters directly for offline recognition.\nradyape\nIn Image acquisition, the recognition system acquires a\nscanned image as an input image. The image should have a\nspecific format such as JPEG, BMP etc. This image is\nacquired through a scanner, digital camera or any other\nsuitable digital input device. Data samples for the\nexperiment have been collected from different individuals\n[9].\n2. Pre Processing\neighteen\neighteen\nThe goal of pre-processing is to simplify the pattern\nrecognition problem without missing any vital information.\nIt reduces the noises and inconsistent data. It enhances the\nimage and prepares it for the next steps [3].\nei gliteen\nFigure 3: Segmentation [9]\nPreprocessing is the preliminary step which transforms the\ndata into a format that will be more easily and effectively\n4. Normalization\nprocessed. Therefore, the main task in preprocessing the\ncaptured data is to decrease the variation that causes a\nreduction in the recognition rate and increases the\nThe results of segmentation process provides isolated\ncharacters which are ready to pass through feature extraction\ncomplexities, as for example, preprocessing of the input raw\nstroke of characters is crucial for the success of efficient\nstage, thus the isolated characters are reduced to a specific\nsize depending on the methods used. The segmentation\ncharacter recognition systems. Thus, preprocessing is an\nprocess essentially renders the image in the form of m*n\nessential stage prior to feature extraction since it controls the\nsuitability of the results for the successive stages [2].\nmatrix. These matrices are then generally normalized by\nreducing the size and removing the redundant information\nfrom the image without losing any important information.\nPreprocessing can be done through various ways\nBinarization, Noise reduction, Stroke width normalization,\n5. Feature Extraction\nSkew correction, Slant removal, Filtering, Morphological\nOperations, Noise Modelling, Skew Normalization, Size\nFeature extraction is the process of extracting the relevant\nNormalization, Contour Smoothing, Compression,\nfeatures from objects/alphabets to form a feature vectors.\nThresholding, Thinning etc\nThese feature vectors is then used by classifiers to recognize\nstart\nthe input unit with target output unit. It becomes easier for\nthe classifier to classify between different classes by looking\nat these features as it allows fairly easy to distinguish.\nstart\nFeature extraction is also defined as extracting the raw data\nthe information which is most relevant for classification\nFigure 1: Slant Removal\npurposes in the sense of minimizing the pattern\nvariability.[1]\nDue to the nature of handwriting with its high degree of\nvariability and imprecision obtaining these features, is a\ndifficult task. Feature extraction methods are based on 3\ntypes of features:\nStatistical\nStructural\nGlobal transformations and moments\nFigure 2: Normalization of 'e' and 'l' as in [9]\nStatistical Features includes:\n3. Segmentation\n1. Zoning\nSegmentation is an integral part of any text based\nrecognition system. It assures efficiency of classification and\nThe character image is divided into NxM zones. From each\nzone features are extracted to form the feature vector. The\nVolume 2 Issue 5, May 2013\nwww.ijsr.net\n156\nInternational Journal of Science and Research (IJSR), India Online ISSN: 2319-7064\ngoal of zoning is to obtain the local characteristics instead of\nglobal characteristics.\nGlobal Transformations-Moments:\nThe Fourier Transform (FT) of the contour of the image is\ncalculated. Since the first n coefficients of the FT can be\nused in order to reconstruct the contour, then these n\ncoefficients are considered to be a n-dimensional feature\nvector that represents the character.\nFigure 4: zoning\nOOSSESSS\nAfter dividing the character into different zones you can\ncompare the density or direction features of it and\nclassify each one of them.\n55555555\n2. Projection Histograms\nFigure 7: Contouring\nThe basic idea behind using projections is that character\n6 Classification\nimages, which are 2-D signals, can be represented as 1-\nD signal. These features, although independent to noise\nThe results Classification is the last stage where we train the\nand deformation, depend on rotation. Projection\nneural net using the feature vectors obtained during feature\nhistograms count the number of pixels in each column\nextraction method against the required targets. To optimize\nand row of a character image. Projection histograms can\nthe whole recognition process, several combination methods\nseparate characters such as \"m\" and \"n\".\nof multilayer perceptron have been devised. E.g.: k-Nearest\nNeighbour (k-NN), Bayes Classifier, Neural Networks (NN),\nHidden Markov Models (HMM), Support Vector Machines\n(SVM), etc there is no such thing as the \"best classifier\". The\nuse of classifier depends on many factors, such as available\ntraining set, number of free parameters etc.\n7. Post Processing\nThe goal of post processing is the incorporation of context\nand shape information in all the stages of OCR systems is\nFigure 5: Projection Histogram\nnecessary for meaningful improvements in recognition rates.\n3. Profiles\n5. Conclusion\nThe profile counts the number of pixels (distance)\nbetween the bounding box of the character image and\nThe character recognition methods have been introduced and\nthe edge of the character. The profiles describe well the\ndeveloped over the years. In this paper, I have tried to\nexternal shapes of characters and allow distinguishing\nexplain the overview of the whole OCR process and the\nbetween a great number of letters, such as \"p\" and \"q\".\nmethods related to it. Many researchers try to hybrid two or\nmore different methods and compare the results for\nefficiency but again this will be application specific and\nparameter specific. OCR has been implemented in various\ncountries for recognizing different languages as well.\nReferences\n[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, \"Feature\nExtraction Methods for Character Recognition-A\nSurvey\", July 1995\nFigure 6: Profiling\n[2] Yasser Alginahi, Taibah University Kingdom of Saudi\nArabia, \"Preprocessing Techniques in Character\n4. Structural features:\nRecognition\"\n[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram\nStructural features are based on topological and geometrical\nShah, Benoy Kumar Thakur, \"Recent Trends and Tools\nproperties of the character, such as aspect ratio, cross points,\nfor Feature Extraction\nloops, branch points, strokes and their directions, inflection\n[4] in OCR Technology\", International Journal of Soft\nbetween two points, horizontal curves at top or bottom, etc.\nComputing and Engineering (IJSCE)\n[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013\nVolume 2 Issue 5, May 2013\nwww.ijsr.net\n157\nInternational Journal of Science and Research (IJSR), India Online ISSN: 2319-7064\n[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok\nExtraction and Classification for English Handwritten\",\nM. Sapkal, \"Survey of Methods for Character\nIJE TRANSACTIONS B: Applications Vol. 25, No. 2,\nRecognition\", International Journal of Engineering and\n(May 2012) 99-106\nInnovative Technology (IJEIT) Volume 1, Issue 5, May\n[12] Nafiz Arica and Fatos T. Yarman-Vural, \"An Overview\n2012\nof Character Recognition Focused on\n[7] Mohanad Alata, Mohammad Al-Shabi \"TEXT [13] Off-Line Handwriting\", IEEE TRANSACTIONS ON\nDETECTION AND CHARACTER\nSYSTEMS, MAN, AND CYBERNETICS-PART C:\n[8] RECOGNITION USING FUZZY IMAGE\nAPPLICATIONS AND REVIEWS, VOL. 31, NO. 2,\nPROCESSING\",\nJournal\nof\nELECTRICAL\nMAY 2001\nENGINEERING, VOL. 57, NO. 5, 2006, 258-267\n[9] Rejean Plamondon, Fellow, IEEE and Sargur N.\nAuthor Profile\nShrihari, Fellow, IEEE, \"On-line and Off-line\nHandwriting Recognition: A comprehensive Survey\",\nUmal Patel, pursuing her Master Degree in Computer\nIEEE TRANSACTIONS ON PATTERN ANALYSIS\nScience & Technology from Gujarat Technological\nAND MACHINE INTELLIGENCE, VOL 22, NO. 1\nUniversity (L D College of Eng., Ahmedabad), received her\nJANUARY 2000\nBachelor Degree in Computer Engg. from Gujarat\n[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram\nUniversity in 2008. Presently working as Assistant Professor\nShah, \"An Improved Zone Based Hybrid Feature\nin Department of MCA, L.J Institute of Technology. Earlier\nExtraction Model for Handwritten Alphabets\nhe has served as Software Test Engineer in Lodestone\nRecognition Using Euler Number\", International Journal\nSoftware Services since June 2008. Her area of interest is\nof Soft Computing and Engineering (IJSCE) ISSN:\nCompilers and Image Processing.\n2231-2307, Volume-2, Issue-2, May 2012\n[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, \"Neural\nNetwork Based Recognition System Integrating Feature\nVolume 2 Issue 5, May 2013\nwww.ijsr.net\n158\n",
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"description": "Available Providers
\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**amazon**|`boto3 (v1.15.18)`|15.0 (per 1000 page)|1 page\n|**google**|`DocumentAI v1 beta3`|65.0 (per 1000 page)|1 page\n|**microsoft**|`rest API 4.0 (2024-02-29-preview)`|10.0 (per 1000 page)|1 page\n\n\nSupported Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Angika**|`anp`|\n|**Arabic**|`ar`|\n|**Asturian**|`ast`|\n|**Awadhi**|`awa`|\n|**Azerbaijani**|`az`|\n|**Bagheli**|`bfy`|\n|**Basque**|`eu`|\n|**Belarusian**|`be`|\n|**Bhojpuri**|`bho`|\n|**Bislama**|`bi`|\n|**Bodo (India)**|`brx`|\n|**Bosnian**|`bs`|\n|**Braj**|`bra`|\n|**Breton**|`br`|\n|**Bulgarian**|`bg`|\n|**Bundeli**|`bns`|\n|**Buriat**|`bua`|\n|**Camling**|`rab`|\n|**Catalan**|`ca`|\n|**Cebuano**|`ceb`|\n|**Chamorro**|`ch`|\n|**Chhattisgarhi**|`hne`|\n|**Chinese**|`zh`|\n|**Cornish**|`kw`|\n|**Corsican**|`co`|\n|**Crimean Tatar**|`crh`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dari**|`prs`|\n|**Dhimal**|`dhi`|\n|**Dogri (macrolanguage)**|`doi`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Erzya**|`myv`|\n|**Estonian**|`et`|\n|**Faroese**|`fo`|\n|**Fijian**|`fj`|\n|**Filipino**|`fil`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Friulian**|`fur`|\n|**Gagauz**|`gag`|\n|**Galician**|`gl`|\n|**German**|`de`|\n|**Gilbertese**|`gil`|\n|**Gondi**|`gon`|\n|**Gurung**|`gvr`|\n|**Haitian**|`ht`|\n|**Halbi**|`hlb`|\n|**Hani**|`hni`|\n|**Haryanvi**|`bgc`|\n|**Hawaiian**|`haw`|\n|**Hindi**|`hi`|\n|**Hmong Daw**|`mww`|\n|**Ho**|`hoc`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Inari Sami**|`smn`|\n|**Indonesian**|`id`|\n|**Interlingua (International Auxiliary Language Association)**|`ia`|\n|**Inuktitut**|`iu`|\n|**Irish**|`ga`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Jaunsari**|`jns`|\n|**Javanese**|`jv`|\n|**K'iche'**|`quc`|\n|**Kabuverdianu**|`kea`|\n|**Kachin**|`kac`|\n|**Kalaallisut**|`kl`|\n|**Kangri**|`xnr`|\n|**Kara-Kalpak**|`kaa`|\n|**Karachay-Balkar**|`krc`|\n|**Kashubian**|`csb`|\n|**Kazakh**|`kk`|\n|**Khaling**|`klr`|\n|**Khasi**|`kha`|\n|**Kirghiz**|`ky`|\n|**Korean**|`ko`|\n|**Korku**|`kfq`|\n|**Koryak**|`kpy`|\n|**Kosraean**|`kos`|\n|**Kumarbhag Paharia**|`kmj`|\n|**Kumyk**|`kum`|\n|**Kurdish**|`ku`|\n|**Kurukh**|`kru`|\n|**Kölsch**|`ksh`|\n|**Lakota**|`lkt`|\n|**Latin**|`la`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Lower Sorbian**|`dsb`|\n|**Lule Sami**|`smj`|\n|**Luxembourgish**|`lb`|\n|**Mahasu Pahari**|`bfz`|\n|**Malay (macrolanguage)**|`ms`|\n|**Maltese**|`mt`|\n|**Manx**|`gv`|\n|**Maori**|`mi`|\n|**Marathi**|`mr`|\n|**Mongolian**|`mn`|\n|**Montenegrin**|`cnr`|\n|**Neapolitan**|`nap`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Niuean**|`niu`|\n|**Nogai**|`nog`|\n|**Northern Sami**|`se`|\n|**Norwegian**|`no`|\n|**Occitan (post 1500)**|`oc`|\n|**Ossetian**|`os`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Pushto**|`ps`|\n|**Romanian**|`ro`|\n|**Romansh**|`rm`|\n|**Russian**|`ru`|\n|**Sadri**|`sck`|\n|**Samoan**|`sm`|\n|**Sanskrit**|`sa`|\n|**Santali**|`sat`|\n|**Scots**|`sco`|\n|**Scottish Gaelic**|`gd`|\n|**Serbian**|`sr`|\n|**Sherpa**|`xsr`|\n|**Sirmauri**|`srx`|\n|**Skolt Sami**|`sms`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Somali**|`so`|\n|**Southern Sami**|`sma`|\n|**Spanish**|`es`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tagalog**|`tl`|\n|**Tajik**|`tg`|\n|**Tatar**|`tt`|\n|**Tetum**|`tet`|\n|**Thangmi**|`thf`|\n|**Tonga (Tonga Islands)**|`to`|\n|**Turkish**|`tr`|\n|**Turkmen**|`tk`|\n|**Tuvinian**|`tyv`|\n|**Uighur**|`ug`|\n|**Upper Sorbian**|`hsb`|\n|**Urdu**|`ur`|\n|**Uzbek**|`uz`|\n|**Vietnamese**|`vi`|\n|**Volapük**|`vo`|\n|**Walser**|`wae`|\n|**Welsh**|`cy`|\n|**Western Frisian**|`fy`|\n|**Yucateco**|`yua`|\n|**Zhuang**|`za`|\n|**Zulu**|`zu`|\n\nSupported Detailed Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Belarusian**|`be-Cyrl`|\n|**Belarusian (Latin)**|`be-Latn`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Chinese (Traditional)**|`zh-Hant`|\n|**Kara-Kalpak (Cyrillic)**|`kaa-Cyrl`|\n|**Kazakh**|`kk-Cyrl`|\n|**Kazakh (Latin)**|`kk-Latn`|\n|**Kurdish (Arabic)**|`ku-Arab`|\n|**Kurdish (Latin)**|`ku-Latn`|\n|**Serbian (Cyrillic)**|`sr-Cyrl`|\n|**Serbian (Cyrillic, Montenegro)**|`sr-Cyrl-ME`|\n|**Serbian (Latin)**|`sr-Latn`|\n|**Serbian (Latin, Montenegro)**|`sr-Latn-ME`|\n|**Uzbek (Arabic)**|`uz-Arab`|\n|**Uzbek (Cyrillic)**|`uz-cyrl`|\n\nAvailable Providers
\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**affinda**|-|`v3`|0.07 (per 1 file)|1 file\n|**klippa**|-|`v1`|0.1 (per 1 file)|1 file\n|**senseloaf**|-|`v3`|0.045 (per 1 file)|1 file\n|**extracta**|-|`v1`|0.1 (per 1 page)|1 page\n|**openai**|-|`v1.0`|0.04 (per 1 page)|1 page\n|**openai**|**gpt-4o**|`v1.0`|0.04 (per 1 page)|1 page\n\n\nSupported Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Amharic**|`am`|\n|**Arabic**|`ar`|\n|**Armenian**|`hy`|\n|**Azerbaijani**|`az`|\n|**Basque**|`eu`|\n|**Belarusian**|`be`|\n|**Bengali**|`bn`|\n|**Bosnian**|`bs`|\n|**Bulgarian**|`bg`|\n|**Burmese**|`my`|\n|**Catalan**|`ca`|\n|**Cebuano**|`ceb`|\n|**Chinese**|`zh`|\n|**Corsican**|`co`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Esperanto**|`eo`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Galician**|`gl`|\n|**Georgian**|`ka`|\n|**German**|`de`|\n|**Gujarati**|`gu`|\n|**Haitian**|`ht`|\n|**Hausa**|`ha`|\n|**Hawaiian**|`haw`|\n|**Hebrew**|`he`|\n|**Hindi**|`hi`|\n|**Hmong**|`hmn`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Igbo**|`ig`|\n|**Indonesian**|`id`|\n|**Irish**|`ga`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Javanese**|`jv`|\n|**Kannada**|`kn`|\n|**Kazakh**|`kk`|\n|**Khmer**|`km`|\n|**Kinyarwanda**|`rw`|\n|**Kirghiz**|`ky`|\n|**Korean**|`ko`|\n|**Kurdish**|`ku`|\n|**Lao**|`lo`|\n|**Latin**|`la`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Luxembourgish**|`lb`|\n|**Macedonian**|`mk`|\n|**Malagasy**|`mg`|\n|**Malay (macrolanguage)**|`ms`|\n|**Malayalam**|`ml`|\n|**Maltese**|`mt`|\n|**Maori**|`mi`|\n|**Marathi**|`mr`|\n|**Modern Greek (1453-)**|`el`|\n|**Mongolian**|`mn`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Norwegian**|`no`|\n|**Nyanja**|`ny`|\n|**Oriya (macrolanguage)**|`or`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Pushto**|`ps`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Samoan**|`sm`|\n|**Scottish Gaelic**|`gd`|\n|**Serbian**|`sr`|\n|**Shona**|`sn`|\n|**Sindhi**|`sd`|\n|**Sinhala**|`si`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Somali**|`so`|\n|**Southern Sotho**|`st`|\n|**Spanish**|`es`|\n|**Sundanese**|`su`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tagalog**|`tl`|\n|**Tajik**|`tg`|\n|**Tamil**|`ta`|\n|**Tatar**|`tt`|\n|**Telugu**|`te`|\n|**Thai**|`th`|\n|**Turkish**|`tr`|\n|**Turkmen**|`tk`|\n|**Uighur**|`ug`|\n|**Ukrainian**|`uk`|\n|**Urdu**|`ur`|\n|**Uzbek**|`uz`|\n|**Vietnamese**|`vi`|\n|**Welsh**|`cy`|\n|**Western Frisian**|`fy`|\n|**Xhosa**|`xh`|\n|**Yiddish**|`yi`|\n|**Yoruba**|`yo`|\n|**Zulu**|`zu`|\n\nSupported Detailed Languages
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (China)**|`zh-CN`|\n|**Chinese (China)**|`zh-cn`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Chinese (Taiwan)**|`zh-tw`|\n\nSupported Models
\n\nDefault Models
\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n\n
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"webhook_receiver": {
"type": "string",
"format": "uri",
"minLength": 1,
"description": "Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint). After the processing is done, the webhook endpoint will receive a POST request with the result."
},
"users_webhook_parameters": {
"description": "Json data that contains of additional parameters that will be sent back to the webhook receiver (ex: api key for security or client's data ID to link the result internally). Will only be used when webhook_receiver is set."
},
"send_webhook_data": {
"type": "boolean",
"default": true,
"description": "If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit."
},
"show_base_64": {
"type": "boolean",
"default": true
},
"provider_params": {
"type": "string",
"description": "\nParameters specific to the provider that you want to send along the request.\n\nit should take a *provider* name as key and an object of parameters as value.\n\nExample:\n\n {\n \"deepgram\": {\n \"filler_words\": true,\n \"smart_format\": true,\n \"callback\": \"https://webhook.site/0000\"\n },\n \"assembly\": {\n \"webhook_url\": \"https://webhook.site/0000\"\n }\n }\n\nPlease refer to the documentation of each provider to see which parameters to send.\n"
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
}
},
"required": [
"providers"
]
},
"AsyncJobList": {
"type": "object",
"properties": {
"providers": {
"type": "string"
},
"nb": {
"type": "integer"
},
"nb_ok": {
"type": "integer"
},
"public_id": {
"type": "string",
"format": "uuid"
},
"state": {
"$ref": "#/components/schemas/StateEnum"
},
"created_at": {
"type": "string",
"format": "date-time"
}
},
"required": [
"created_at",
"nb",
"nb_ok",
"providers",
"public_id",
"state"
]
},
"AsyncOcrRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"webhook_receiver": {
"type": "string",
"format": "uri",
"minLength": 1,
"description": "Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint). After the processing is done, the webhook endpoint will receive a POST request with the result."
},
"users_webhook_parameters": {
"description": "Json data that contains of additional parameters that will be sent back to the webhook receiver (ex: api key for security or client's data ID to link the result internally). Will only be used when webhook_receiver is set."
},
"send_webhook_data": {
"type": "boolean",
"default": true,
"description": "If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit."
},
"show_base_64": {
"type": "boolean",
"default": true
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
}
},
"required": [
"providers"
]
},
"BadRequest": {
"type": "object",
"properties": {
"error": {
"$ref": "#/components/schemas/NestedBadRequest"
}
},
"required": [
"error"
]
},
"BoundingBox": {
"description": "Bounding box of a word in the image\n\nAttributes:\n left (float): Left coordinate of the bounding box\n top (float): Top coordinate of the bounding box\n width (float): Width of the bounding box\n height (float): Height of the bounding box\n text (str): Text detected in the bounding box\n\nConstructor:\n from_json (classmethod): Create a new instance of BoundingBox from a JSON object\n from_normalized_vertices (classmethod): Create a new instance of BoundingBox from normalized vertices\n unknown (classmethod): Return a invalid bouding_box with all field filled with `-1`",
"properties": {
"left": {
"description": "Left coordinate of the bounding box",
"title": "Left",
"type": "integer"
},
"top": {
"description": "Top coordinate of the bounding box",
"title": "Top",
"type": "integer"
},
"width": {
"description": "Width of the bounding box",
"title": "Width",
"type": "integer"
},
"height": {
"description": "Height of the bounding box",
"title": "Height",
"type": "integer"
}
},
"required": [
"left",
"top",
"width",
"height"
],
"title": "BoundingBox",
"type": "object"
},
"Bounding_box": {
"properties": {
"text": {
"title": "Text",
"type": "string"
},
"left": {
"title": "Left",
"type": "integer"
},
"top": {
"title": "Top",
"type": "integer"
},
"width": {
"title": "Width",
"type": "integer"
},
"height": {
"title": "Height",
"type": "integer"
}
},
"required": [
"text",
"left",
"top",
"width",
"height"
],
"title": "Bounding_box",
"type": "object"
},
"BoundixBoxOCRTable": {
"properties": {
"left": {
"title": "Left",
"type": "integer"
},
"top": {
"title": "Top",
"type": "integer"
},
"width": {
"title": "Width",
"type": "integer"
},
"height": {
"title": "Height",
"type": "integer"
}
},
"required": [
"left",
"top",
"width",
"height"
],
"title": "BoundixBoxOCRTable",
"type": "object"
},
"Cell": {
"properties": {
"text": {
"title": "Text",
"type": "string"
},
"row_index": {
"title": "Row Index",
"type": "integer"
},
"col_index": {
"title": "Col Index",
"type": "integer"
},
"row_span": {
"title": "Row Span",
"type": "integer"
},
"col_span": {
"title": "Col Span",
"type": "integer"
},
"confidence": {
"title": "Confidence",
"type": "integer"
},
"bounding_box": {
"$ref": "#/components/schemas/BoundixBoxOCRTable"
},
"is_header": {
"default": false,
"title": "Is Header",
"type": "boolean"
}
},
"required": [
"text",
"row_index",
"col_index",
"row_span",
"col_span",
"confidence",
"bounding_box"
],
"title": "Cell",
"type": "object"
},
"Country": {
"properties": {
"name": {
"title": "Name",
"type": "string"
},
"alpha2": {
"title": "Alpha2",
"type": "string"
},
"alpha3": {
"title": "Alpha3",
"type": "string"
},
"confidence": {
"default": null,
"title": "Confidence",
"type": "integer"
}
},
"required": [
"name",
"alpha2",
"alpha3"
],
"title": "Country",
"type": "object"
},
"CustomDocumentParsingAsyncBoundingBox": {
"properties": {
"left": {
"title": "Left",
"type": "integer"
},
"top": {
"title": "Top",
"type": "integer"
},
"width": {
"title": "Width",
"type": "integer"
},
"height": {
"title": "Height",
"type": "integer"
}
},
"required": [
"left",
"top",
"width",
"height"
],
"title": "CustomDocumentParsingAsyncBoundingBox",
"type": "object"
},
"CustomDocumentParsingAsyncItem": {
"properties": {
"confidence": {
"title": "Confidence",
"type": "integer"
},
"value": {
"title": "Value",
"type": "string"
},
"query": {
"title": "Query",
"type": "string"
},
"bounding_box": {
"$ref": "#/components/schemas/CustomDocumentParsingAsyncBoundingBox"
},
"page": {
"title": "Page",
"type": "integer"
}
},
"required": [
"confidence",
"value",
"query",
"bounding_box",
"page"
],
"title": "CustomDocumentParsingAsyncItem",
"type": "object"
},
"CustomDocumentParsingAsyncRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"webhook_receiver": {
"type": "string",
"format": "uri",
"minLength": 1,
"description": "Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint). After the processing is done, the webhook endpoint will receive a POST request with the result."
},
"users_webhook_parameters": {
"description": "Json data that contains of additional parameters that will be sent back to the webhook receiver (ex: api key for security or client's data ID to link the result internally). Will only be used when webhook_receiver is set."
},
"send_webhook_data": {
"type": "boolean",
"default": true,
"description": "If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit."
},
"show_base_64": {
"type": "boolean",
"default": true
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"queries": {
"type": "string",
"minLength": 1,
"description": "Your queries need to be a list of dict containing the questions you want answered and the page to look for the information in : '[{'query':'your query','pages':'your pages'},{'query':'your query','pages':'your pages'}]'"
},
"convert_to_pdf": {
"type": "boolean",
"nullable": true,
"default": false,
"description": "Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers"
}
},
"required": [
"providers",
"queries"
]
},
"DocumentTypeEnum": {
"enum": [
"auto-detect",
"invoice",
"receipt"
],
"type": "string",
"description": "* `auto-detect` - auto-detect\n* `invoice` - invoice\n* `receipt` - receipt"
},
"Error": {
"type": "object",
"properties": {
"error": {
"$ref": "#/components/schemas/NestedError"
}
},
"required": [
"error"
]
},
"FieldError": {
"type": "object",
"properties": {
"": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
""
]
},
"FinalStatusEnum": {
"enum": [
"sucess",
"fail"
],
"type": "string"
},
"FinancialBankInformation": {
"properties": {
"iban": {
"default": null,
"description": "International Bank Account Number.",
"title": "Iban",
"type": "string"
},
"swift": {
"default": null,
"description": "Society for Worldwide Interbank Financial Telecommunication code.",
"title": "Swift",
"type": "string"
},
"bsb": {
"default": null,
"description": "Bank State Branch code (Australia).",
"title": "Bsb",
"type": "string"
},
"sort_code": {
"default": null,
"description": "Sort code for UK banks.",
"title": "Sort Code",
"type": "string"
},
"account_number": {
"default": null,
"description": "Bank account number.",
"title": "Account Number",
"type": "string"
},
"routing_number": {
"default": null,
"description": "Routing number for banks in the United States.",
"title": "Routing Number",
"type": "string"
},
"bic": {
"default": null,
"description": "Bank Identifier Code.",
"title": "Bic",
"type": "string"
}
},
"title": "FinancialBankInformation",
"type": "object"
},
"FinancialBarcode": {
"properties": {
"value": {
"title": "Value",
"type": "string"
},
"type": {
"title": "Type",
"type": "string"
}
},
"required": [
"value",
"type"
],
"title": "FinancialBarcode",
"type": "object"
},
"FinancialCustomerInformation": {
"properties": {
"name": {
"default": null,
"description": "The name of the invoiced customer.",
"title": "Name",
"type": "string"
},
"id_reference": {
"default": null,
"description": "Unique reference ID for the customer.",
"title": "Id Reference",
"type": "string"
},
"mailling_address": {
"default": null,
"description": "The mailing address of the customer.",
"title": "Mailling Address",
"type": "string"
},
"billing_address": {
"default": null,
"description": "The explicit billing address for the customer.",
"title": "Billing Address",
"type": "string"
},
"shipping_address": {
"default": null,
"description": "The shipping address for the customer.",
"title": "Shipping Address",
"type": "string"
},
"service_address": {
"default": null,
"description": "The service address associated with the customer.",
"title": "Service Address",
"type": "string"
},
"remittance_address": {
"default": null,
"description": "The address to which payments should be remitted.",
"title": "Remittance Address",
"type": "string"
},
"email": {
"default": null,
"description": "The email address of the customer.",
"title": "Email",
"type": "string"
},
"phone": {
"default": null,
"description": "The phone number associated with the customer.",
"title": "Phone",
"type": "string"
},
"vat_number": {
"default": null,
"description": "VAT (Value Added Tax) number of the customer.",
"title": "Vat Number",
"type": "string"
},
"abn_number": {
"default": null,
"description": "ABN (Australian Business Number) of the customer.",
"title": "Abn Number",
"type": "string"
},
"gst_number": {
"default": null,
"description": "GST (Goods and Services Tax) number of the customer.",
"title": "Gst Number",
"type": "string"
},
"pan_number": {
"default": null,
"description": "PAN (Permanent Account Number) of the customer.",
"title": "Pan Number",
"type": "string"
},
"business_number": {
"default": null,
"description": "Business registration number of the customer.",
"title": "Business Number",
"type": "string"
},
"siret_number": {
"default": null,
"description": "SIRET (Système d'Identification du Répertoire des Entreprises et de leurs Établissements) number of the customer.",
"title": "Siret Number",
"type": "string"
},
"siren_number": {
"default": null,
"description": "SIREN (Système d'Identification du Répertoire des Entreprises) number of the customer.",
"title": "Siren Number",
"type": "string"
},
"customer_number": {
"default": null,
"description": "Customer identification number.",
"title": "Customer Number",
"type": "string"
},
"coc_number": {
"default": null,
"description": "Chamber of Commerce registration number.",
"title": "Coc Number",
"type": "string"
},
"fiscal_number": {
"default": null,
"description": "Fiscal identification number of the customer.",
"title": "Fiscal Number",
"type": "string"
},
"registration_number": {
"default": null,
"description": "Official registration number of the customer.",
"title": "Registration Number",
"type": "string"
},
"tax_id": {
"default": null,
"description": "Tax identification number of the customer.",
"title": "Tax Id",
"type": "string"
},
"website": {
"default": null,
"description": "The website associated with the customer.",
"title": "Website",
"type": "string"
},
"remit_to_name": {
"default": null,
"description": "The name associated with the customer's remittance address.",
"title": "Remit To Name",
"type": "string"
},
"city": {
"default": null,
"description": "The city associated with the customer's address.",
"title": "City",
"type": "string"
},
"country": {
"default": null,
"description": "The country associated with the customer's address.",
"title": "Country",
"type": "string"
},
"house_number": {
"default": null,
"description": "The house number associated with the customer's address.",
"title": "House Number",
"type": "string"
},
"province": {
"default": null,
"description": "The province associated with the customer's address.",
"title": "Province",
"type": "string"
},
"street_name": {
"default": null,
"description": "The street name associated with the customer's address.",
"title": "Street Name",
"type": "string"
},
"zip_code": {
"default": null,
"description": "The ZIP code associated with the customer's address.",
"title": "Zip Code",
"type": "string"
},
"municipality": {
"default": null,
"description": "The municipality associated with the customer's address.",
"title": "Municipality",
"type": "string"
}
},
"title": "FinancialCustomerInformation",
"type": "object"
},
"FinancialDocumentInformation": {
"properties": {
"invoice_receipt_id": {
"default": null,
"description": "Identifier for the invoice.",
"title": "Invoice Receipt Id",
"type": "string"
},
"purchase_order": {
"default": null,
"description": "Purchase order related to the document.",
"title": "Purchase Order",
"type": "string"
},
"invoice_date": {
"default": null,
"description": "Date of the invoice.",
"title": "Invoice Date",
"type": "string"
},
"time": {
"default": null,
"description": "Time associated with the document.",
"title": "Time",
"type": "string"
},
"invoice_due_date": {
"default": null,
"description": "Due date for the invoice.",
"title": "Invoice Due Date",
"type": "string"
},
"service_start_date": {
"default": null,
"description": "Start date of the service associated with the document.",
"title": "Service Start Date",
"type": "string"
},
"service_end_date": {
"default": null,
"description": "End date of the service associated with the document.",
"title": "Service End Date",
"type": "string"
},
"reference": {
"default": null,
"description": "Reference number associated with the document.",
"title": "Reference",
"type": "string"
},
"biller_code": {
"default": null,
"description": "Biller code associated with the document.",
"title": "Biller Code",
"type": "string"
},
"order_date": {
"default": null,
"description": "Date of the order associated with the document.",
"title": "Order Date",
"type": "string"
},
"tracking_number": {
"default": null,
"description": "Tracking number associated with the document.",
"title": "Tracking Number",
"type": "string"
},
"barcodes": {
"description": "List of barcodes associated with the document.",
"items": {
"$ref": "#/components/schemas/FinancialBarcode"
},
"title": "Barcodes",
"type": "array"
}
},
"title": "FinancialDocumentInformation",
"type": "object"
},
"FinancialDocumentMetadata": {
"properties": {
"document_index": {
"default": null,
"description": "Index of the detected document.",
"title": "Document Index",
"type": "integer"
},
"document_page_number": {
"default": null,
"description": "Page number within the document.",
"title": "Document Page Number",
"type": "integer"
},
"document_type": {
"default": null,
"description": "Type or category of the document.",
"title": "Document Type",
"type": "string"
}
},
"title": "FinancialDocumentMetadata",
"type": "object"
},
"FinancialLineItem": {
"properties": {
"tax": {
"default": null,
"description": "Tax amount for the line item.",
"title": "Tax",
"type": "integer"
},
"amount_line": {
"default": null,
"description": "Total amount for the line item.",
"title": "Amount Line",
"type": "integer"
},
"description": {
"default": null,
"description": "Description of the line item.",
"title": "Description",
"type": "string"
},
"quantity": {
"default": null,
"description": "Quantity of units for the line item.",
"title": "Quantity",
"type": "integer"
},
"unit_price": {
"default": null,
"description": "Unit price for each unit in the line item.",
"title": "Unit Price",
"type": "integer"
},
"unit_type": {
"default": null,
"description": "Type of unit (e.g., hours, items).",
"title": "Unit Type",
"type": "string"
},
"date": {
"default": null,
"description": "Date associated with the line item.",
"title": "Date",
"type": "string"
},
"product_code": {
"default": null,
"description": "Product code or identifier for the line item.",
"title": "Product Code",
"type": "string"
},
"purchase_order": {
"default": null,
"description": "Purchase order related to the line item.",
"title": "Purchase Order",
"type": "string"
},
"tax_rate": {
"default": null,
"description": "Tax rate applied to the line item.",
"title": "Tax Rate",
"type": "integer"
},
"base_total": {
"default": null,
"description": "Base total amount before any discounts or taxes.",
"title": "Base Total",
"type": "integer"
},
"sub_total": {
"default": null,
"description": "Subtotal amount for the line item.",
"title": "Sub Total",
"type": "integer"
},
"discount_amount": {
"default": null,
"description": "Amount of discount applied to the line item.",
"title": "Discount Amount",
"type": "integer"
},
"discount_rate": {
"default": null,
"description": "Rate of discount applied to the line item.",
"title": "Discount Rate",
"type": "integer"
},
"discount_code": {
"default": null,
"description": "Code associated with any discount applied to the line item.",
"title": "Discount Code",
"type": "string"
},
"order_number": {
"default": null,
"description": "Order number associated with the line item.",
"title": "Order Number",
"type": "string"
},
"title": {
"default": null,
"description": "Title or name of the line item.",
"title": "Title",
"type": "string"
}
},
"title": "FinancialLineItem",
"type": "object"
},
"FinancialLocalInformation": {
"properties": {
"currency": {
"default": null,
"description": "Currency used in financial transactions.",
"title": "Currency",
"type": "string"
},
"currency_code": {
"default": null,
"description": "Currency code (e.g., USD, EUR).",
"title": "Currency Code",
"type": "string"
},
"currency_exchange_rate": {
"default": null,
"description": "Exchange rate for the specified currency.",
"title": "Currency Exchange Rate",
"type": "string"
},
"country": {
"default": null,
"description": "Country associated with the local financial information.",
"title": "Country",
"type": "string"
},
"language": {
"default": null,
"description": "Language used in financial transactions.",
"title": "Language",
"type": "string"
}
},
"title": "FinancialLocalInformation",
"type": "object"
},
"FinancialMerchantInformation": {
"properties": {
"name": {
"default": null,
"description": "Name of the merchant.",
"title": "Name",
"type": "string"
},
"address": {
"default": null,
"description": "Address of the merchant.",
"title": "Address",
"type": "string"
},
"phone": {
"default": null,
"description": "Phone number of the merchant.",
"title": "Phone",
"type": "string"
},
"tax_id": {
"default": null,
"description": "Tax identification number of the merchant.",
"title": "Tax Id",
"type": "string"
},
"id_reference": {
"default": null,
"description": "Unique reference ID for the merchant.",
"title": "Id Reference",
"type": "string"
},
"vat_number": {
"default": null,
"description": "VAT (Value Added Tax) number of the merchant.",
"title": "Vat Number",
"type": "string"
},
"abn_number": {
"default": null,
"description": "ABN (Australian Business Number) of the merchant.",
"title": "Abn Number",
"type": "string"
},
"gst_number": {
"default": null,
"description": "GST (Goods and Services Tax) number of the merchant.",
"title": "Gst Number",
"type": "string"
},
"business_number": {
"default": null,
"description": "Business registration number of the merchant.",
"title": "Business Number",
"type": "string"
},
"siret_number": {
"default": null,
"description": "SIRET (Système d'Identification du Répertoire des Entreprises et de leurs Établissements) number of the merchant.",
"title": "Siret Number",
"type": "string"
},
"siren_number": {
"default": null,
"description": "SIREN (Système d'Identification du Répertoire des Entreprises) number of the merchant.",
"title": "Siren Number",
"type": "string"
},
"pan_number": {
"default": null,
"description": "PAN (Permanent Account Number) of the merchant.",
"title": "Pan Number",
"type": "string"
},
"coc_number": {
"default": null,
"description": "Chamber of Commerce registration number of the merchant.",
"title": "Coc Number",
"type": "string"
},
"fiscal_number": {
"default": null,
"description": "Fiscal identification number of the merchant.",
"title": "Fiscal Number",
"type": "string"
},
"email": {
"default": null,
"description": "Email address of the merchant.",
"title": "Email",
"type": "string"
},
"fax": {
"default": null,
"description": "Fax number of the merchant.",
"title": "Fax",
"type": "string"
},
"website": {
"default": null,
"description": "Website of the merchant.",
"title": "Website",
"type": "string"
},
"registration": {
"default": null,
"description": "Official registration information of the merchant.",
"title": "Registration",
"type": "string"
},
"city": {
"default": null,
"description": "City associated with the merchant's address.",
"title": "City",
"type": "string"
},
"country": {
"default": null,
"description": "Country associated with the merchant's address.",
"title": "Country",
"type": "string"
},
"house_number": {
"default": null,
"description": "House number associated with the merchant's address.",
"title": "House Number",
"type": "string"
},
"province": {
"default": null,
"description": "Province associated with the merchant's address.",
"title": "Province",
"type": "string"
},
"street_name": {
"default": null,
"description": "Street name associated with the merchant's address.",
"title": "Street Name",
"type": "string"
},
"zip_code": {
"default": null,
"description": "ZIP code associated with the merchant's address.",
"title": "Zip Code",
"type": "string"
},
"country_code": {
"default": null,
"description": "Country code associated with the merchant's location.",
"title": "Country Code",
"type": "string"
}
},
"title": "FinancialMerchantInformation",
"type": "object"
},
"FinancialParserObjectDataClass": {
"properties": {
"customer_information": {
"$ref": "#/components/schemas/FinancialCustomerInformation"
},
"merchant_information": {
"$ref": "#/components/schemas/FinancialMerchantInformation"
},
"payment_information": {
"$ref": "#/components/schemas/FinancialPaymentInformation"
},
"financial_document_information": {
"$ref": "#/components/schemas/FinancialDocumentInformation"
},
"local": {
"$ref": "#/components/schemas/FinancialLocalInformation"
},
"bank": {
"$ref": "#/components/schemas/FinancialBankInformation"
},
"item_lines": {
"description": "List of line items associated with the document.",
"items": {
"$ref": "#/components/schemas/FinancialLineItem"
},
"title": "Item Lines",
"type": "array"
},
"document_metadata": {
"$ref": "#/components/schemas/FinancialDocumentMetadata"
}
},
"required": [
"customer_information",
"merchant_information",
"payment_information",
"financial_document_information",
"local",
"bank",
"document_metadata"
],
"title": "FinancialParserObjectDataClass",
"type": "object"
},
"FinancialPaymentInformation": {
"properties": {
"amount_due": {
"default": null,
"description": "Amount due for payment.",
"title": "Amount Due",
"type": "integer"
},
"amount_tip": {
"default": null,
"description": "Tip amount in a financial transaction.",
"title": "Amount Tip",
"type": "integer"
},
"amount_shipping": {
"default": null,
"description": "Shipping cost in a financial transaction.",
"title": "Amount Shipping",
"type": "integer"
},
"amount_change": {
"default": null,
"description": "Change amount in a financial transaction.",
"title": "Amount Change",
"type": "integer"
},
"amount_paid": {
"default": null,
"description": "Amount already paid in a financial transaction.",
"title": "Amount Paid",
"type": "integer"
},
"total": {
"default": null,
"description": "Total amount in the invoice.",
"title": "Total",
"type": "integer"
},
"subtotal": {
"default": null,
"description": "Subtotal amount in a financial transaction.",
"title": "Subtotal",
"type": "integer"
},
"total_tax": {
"default": null,
"description": "Total tax amount in a financial transaction.",
"title": "Total Tax",
"type": "integer"
},
"tax_rate": {
"default": null,
"description": "Tax rate applied in a financial transaction.",
"title": "Tax Rate",
"type": "integer"
},
"discount": {
"default": null,
"description": "Discount amount applied in a financial transaction.",
"title": "Discount",
"type": "integer"
},
"gratuity": {
"default": null,
"description": "Gratuity amount in a financial transaction.",
"title": "Gratuity",
"type": "integer"
},
"service_charge": {
"default": null,
"description": "Service charge in a financial transaction.",
"title": "Service Charge",
"type": "integer"
},
"previous_unpaid_balance": {
"default": null,
"description": "Previous unpaid balance in a financial transaction.",
"title": "Previous Unpaid Balance",
"type": "integer"
},
"prior_balance": {
"default": null,
"description": "Prior balance before the current financial transaction.",
"title": "Prior Balance",
"type": "integer"
},
"payment_terms": {
"default": null,
"description": "Terms and conditions for payment.",
"title": "Payment Terms",
"type": "string"
},
"payment_method": {
"default": null,
"description": "Payment method used in the financial transaction.",
"title": "Payment Method",
"type": "string"
},
"payment_card_number": {
"default": null,
"description": "Card number used in the payment.",
"title": "Payment Card Number",
"type": "string"
},
"payment_auth_code": {
"default": null,
"description": "Authorization code for the payment.",
"title": "Payment Auth Code",
"type": "string"
},
"shipping_handling_charge": {
"default": null,
"description": "Charge for shipping and handling in a financial transaction.",
"title": "Shipping Handling Charge",
"type": "integer"
},
"transaction_number": {
"default": null,
"description": "Unique identifier for the financial transaction.",
"title": "Transaction Number",
"type": "string"
},
"transaction_reference": {
"default": null,
"description": "Reference number for the financial transaction.",
"title": "Transaction Reference",
"type": "string"
}
},
"title": "FinancialPaymentInformation",
"type": "object"
},
"InfosIdentityParserDataClass": {
"properties": {
"last_name": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"given_names": {
"items": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"title": "Given Names",
"type": "array"
},
"birth_place": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"birth_date": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"issuance_date": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"expire_date": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"document_id": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"issuing_state": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"address": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"age": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"country": {
"$ref": "#/components/schemas/Country"
},
"document_type": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"gender": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"image_id": {
"items": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"title": "Image Id",
"type": "array"
},
"image_signature": {
"items": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"title": "Image Signature",
"type": "array"
},
"mrz": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
},
"nationality": {
"$ref": "#/components/schemas/ItemIdentityParserDataClass"
}
},
"required": [
"last_name",
"birth_place",
"birth_date",
"issuance_date",
"expire_date",
"document_id",
"issuing_state",
"address",
"age",
"country",
"document_type",
"gender",
"mrz",
"nationality"
],
"title": "InfosIdentityParserDataClass",
"type": "object"
},
"ItemBankCheckParsingDataClass": {
"properties": {
"amount": {
"title": "Amount",
"type": "integer"
},
"amount_text": {
"title": "Amount Text",
"type": "string"
},
"bank_address": {
"title": "Bank Address",
"type": "string"
},
"bank_name": {
"title": "Bank Name",
"type": "string"
},
"date": {
"title": "Date",
"type": "string"
},
"memo": {
"title": "Memo",
"type": "string"
},
"payer_address": {
"title": "Payer Address",
"type": "string"
},
"payer_name": {
"title": "Payer Name",
"type": "string"
},
"receiver_address": {
"title": "Receiver Address",
"type": "string"
},
"receiver_name": {
"title": "Receiver Name",
"type": "string"
},
"currency": {
"title": "Currency",
"type": "string"
},
"micr": {
"$ref": "#/components/schemas/MicrModel"
}
},
"required": [
"amount",
"amount_text",
"bank_address",
"bank_name",
"date",
"memo",
"payer_address",
"payer_name",
"receiver_address",
"receiver_name",
"currency",
"micr"
],
"title": "ItemBankCheckParsingDataClass",
"type": "object"
},
"ItemDataExtraction": {
"properties": {
"key": {
"title": "Key",
"type": "string"
},
"value": {
"title": "Value"
},
"bounding_box": {
"$ref": "#/components/schemas/BoundingBox"
},
"confidence_score": {
"maximum": 1,
"minimum": 0,
"title": "Confidence Score",
"type": "integer"
}
},
"required": [
"key",
"value",
"bounding_box",
"confidence_score"
],
"title": "ItemDataExtraction",
"type": "object"
},
"ItemIdentityParserDataClass": {
"properties": {
"value": {
"default": null,
"title": "Value",
"type": "string"
},
"confidence": {
"default": null,
"title": "Confidence",
"type": "integer"
}
},
"title": "ItemIdentityParserDataClass",
"type": "object"
},
"LaunchAsyncJobResponse": {
"type": "object",
"properties": {
"public_id": {
"type": "string",
"format": "uuid"
}
},
"required": [
"public_id"
]
},
"Line": {
"description": "Line of a document\n\nAttributes:\n text (str): Text detected in the line\n bounding_boxes (Sequence[BoundingBox]): Bounding boxes of the words in the line\n words (Sequence[Word]): List of words of the line\n confidence (float): Confidence of the line",
"properties": {
"text": {
"description": "Text detected in the line",
"title": "Text",
"type": "string"
},
"words": {
"description": "List of words",
"items": {
"$ref": "#/components/schemas/Word"
},
"title": "Words",
"type": "array"
},
"bounding_box": {
"$ref": "#/components/schemas/BoundingBox",
"default": null,
"description": "Bounding box of the line, can be None"
},
"confidence": {
"description": "Confidence of the line",
"title": "Confidence",
"type": "integer"
}
},
"required": [
"text",
"confidence"
],
"title": "Line",
"type": "object"
},
"ListAsyncJobResponse": {
"type": "object",
"properties": {
"jobs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AsyncJobList"
}
}
},
"required": [
"jobs"
]
},
"MicrModel": {
"properties": {
"raw": {
"title": "Raw",
"type": "string"
},
"account_number": {
"title": "Account Number",
"type": "string"
},
"routing_number": {
"title": "Routing Number",
"type": "string"
},
"serial_number": {
"title": "Serial Number",
"type": "string"
},
"check_number": {
"title": "Check Number",
"type": "string"
}
},
"required": [
"raw",
"account_number",
"routing_number",
"serial_number",
"check_number"
],
"title": "MicrModel",
"type": "object"
},
"NestedBadRequest": {
"type": "object",
"properties": {
"type": {
"type": "string"
},
"message": {
"$ref": "#/components/schemas/FieldError"
}
},
"required": [
"message",
"type"
]
},
"NestedError": {
"type": "object",
"properties": {
"type": {
"type": "string"
},
"message": {
"type": "string"
}
},
"required": [
"message",
"type"
]
},
"NotFoundResponse": {
"type": "object",
"properties": {
"details": {
"type": "string",
"default": "Not Found"
}
}
},
"OcrTablesAsyncRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"webhook_receiver": {
"type": "string",
"format": "uri",
"minLength": 1,
"description": "Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint). After the processing is done, the webhook endpoint will receive a POST request with the result."
},
"users_webhook_parameters": {
"description": "Json data that contains of additional parameters that will be sent back to the webhook receiver (ex: api key for security or client's data ID to link the result internally). Will only be used when webhook_receiver is set."
},
"send_webhook_data": {
"type": "boolean",
"default": true,
"description": "If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit."
},
"show_base_64": {
"type": "boolean",
"default": true
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"language": {
"type": "string",
"nullable": true,
"description": "Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))"
}
},
"required": [
"providers"
]
},
"Page": {
"description": "Page of a document\n\nAttributes:\n lines (Sequence[Line]): List of lines of the page",
"properties": {
"lines": {
"description": "List of lines",
"items": {
"$ref": "#/components/schemas/Line"
},
"title": "Lines",
"type": "array"
}
},
"title": "Page",
"type": "object"
},
"ResumeEducation": {
"properties": {
"total_years_education": {
"title": "Total Years Education",
"type": "integer"
},
"entries": {
"items": {
"$ref": "#/components/schemas/ResumeEducationEntry"
},
"title": "Entries",
"type": "array"
}
},
"required": [
"total_years_education"
],
"title": "ResumeEducation",
"type": "object"
},
"ResumeEducationEntry": {
"properties": {
"title": {
"title": "Title",
"type": "string"
},
"start_date": {
"title": "Start Date",
"type": "string"
},
"end_date": {
"title": "End Date",
"type": "string"
},
"location": {
"$ref": "#/components/schemas/ResumeLocation"
},
"establishment": {
"title": "Establishment",
"type": "string"
},
"description": {
"title": "Description",
"type": "string"
},
"gpa": {
"title": "Gpa",
"type": "string"
},
"accreditation": {
"title": "Accreditation",
"type": "string"
}
},
"required": [
"title",
"start_date",
"end_date",
"location",
"establishment",
"description",
"gpa",
"accreditation"
],
"title": "ResumeEducationEntry",
"type": "object"
},
"ResumeExtractedData": {
"properties": {
"personal_infos": {
"$ref": "#/components/schemas/ResumePersonalInfo"
},
"education": {
"$ref": "#/components/schemas/ResumeEducation"
},
"work_experience": {
"$ref": "#/components/schemas/ResumeWorkExp"
},
"languages": {
"items": {
"$ref": "#/components/schemas/ResumeLang"
},
"title": "Languages",
"type": "array"
},
"skills": {
"items": {
"$ref": "#/components/schemas/ResumeSkill"
},
"title": "Skills",
"type": "array"
},
"certifications": {
"items": {
"$ref": "#/components/schemas/ResumeSkill"
},
"title": "Certifications",
"type": "array"
},
"courses": {
"items": {
"$ref": "#/components/schemas/ResumeSkill"
},
"title": "Courses",
"type": "array"
},
"publications": {
"items": {
"$ref": "#/components/schemas/ResumeSkill"
},
"title": "Publications",
"type": "array"
},
"interests": {
"items": {
"$ref": "#/components/schemas/ResumeSkill"
},
"title": "Interests",
"type": "array"
}
},
"required": [
"personal_infos",
"education",
"work_experience"
],
"title": "ResumeExtractedData",
"type": "object"
},
"ResumeLang": {
"properties": {
"name": {
"title": "Name",
"type": "string"
},
"code": {
"title": "Code",
"type": "string"
}
},
"required": [
"name",
"code"
],
"title": "ResumeLang",
"type": "object"
},
"ResumeLocation": {
"properties": {
"formatted_location": {
"title": "Formatted Location",
"type": "string"
},
"postal_code": {
"title": "Postal Code",
"type": "string"
},
"region": {
"title": "Region",
"type": "string"
},
"country": {
"title": "Country",
"type": "string"
},
"country_code": {
"title": "Country Code",
"type": "string"
},
"raw_input_location": {
"title": "Raw Input Location",
"type": "string"
},
"street": {
"title": "Street",
"type": "string"
},
"street_number": {
"title": "Street Number",
"type": "string"
},
"appartment_number": {
"title": "Appartment Number",
"type": "string"
},
"city": {
"title": "City",
"type": "string"
}
},
"required": [
"formatted_location",
"postal_code",
"region",
"country",
"country_code",
"raw_input_location",
"street",
"street_number",
"appartment_number",
"city"
],
"title": "ResumeLocation",
"type": "object"
},
"ResumePersonalInfo": {
"properties": {
"name": {
"$ref": "#/components/schemas/ResumePersonalName"
},
"address": {
"$ref": "#/components/schemas/ResumeLocation"
},
"self_summary": {
"title": "Self Summary",
"type": "string"
},
"objective": {
"title": "Objective",
"type": "string"
},
"date_of_birth": {
"title": "Date Of Birth",
"type": "string"
},
"place_of_birth": {
"title": "Place Of Birth",
"type": "string"
},
"phones": {
"items": {
"type": "string"
},
"title": "Phones",
"type": "array"
},
"mails": {
"items": {
"type": "string"
},
"title": "Mails",
"type": "array"
},
"urls": {
"items": {
"type": "string"
},
"title": "Urls",
"type": "array"
},
"fax": {
"items": {
"type": "string"
},
"title": "Fax",
"type": "array"
},
"current_profession": {
"title": "Current Profession",
"type": "string"
},
"gender": {
"title": "Gender",
"type": "string"
},
"nationality": {
"title": "Nationality",
"type": "string"
},
"martial_status": {
"title": "Martial Status",
"type": "string"
},
"current_salary": {
"title": "Current Salary",
"type": "string"
},
"availability": {
"default": null,
"title": "Availability",
"type": "string"
}
},
"required": [
"name",
"address",
"self_summary",
"objective",
"date_of_birth",
"place_of_birth",
"current_profession",
"gender",
"nationality",
"martial_status",
"current_salary"
],
"title": "ResumePersonalInfo",
"type": "object"
},
"ResumePersonalName": {
"properties": {
"first_name": {
"title": "First Name",
"type": "string"
},
"last_name": {
"title": "Last Name",
"type": "string"
},
"raw_name": {
"title": "Raw Name",
"type": "string"
},
"middle": {
"title": "Middle",
"type": "string"
},
"title": {
"title": "Title",
"type": "string"
},
"prefix": {
"title": "Prefix",
"type": "string"
},
"sufix": {
"title": "Sufix",
"type": "string"
}
},
"required": [
"first_name",
"last_name",
"raw_name",
"middle",
"title",
"prefix",
"sufix"
],
"title": "ResumePersonalName",
"type": "object"
},
"ResumeSkill": {
"properties": {
"name": {
"title": "Name",
"type": "string"
},
"type": {
"title": "Type",
"type": "string"
}
},
"required": [
"name",
"type"
],
"title": "ResumeSkill",
"type": "object"
},
"ResumeWorkExp": {
"properties": {
"total_years_experience": {
"title": "Total Years Experience",
"type": "string"
},
"entries": {
"items": {
"$ref": "#/components/schemas/ResumeWorkExpEntry"
},
"title": "Entries",
"type": "array"
}
},
"required": [
"total_years_experience"
],
"title": "ResumeWorkExp",
"type": "object"
},
"ResumeWorkExpEntry": {
"properties": {
"title": {
"title": "Title",
"type": "string"
},
"start_date": {
"title": "Start Date",
"type": "string"
},
"end_date": {
"title": "End Date",
"type": "string"
},
"company": {
"title": "Company",
"type": "string"
},
"location": {
"$ref": "#/components/schemas/ResumeLocation"
},
"description": {
"title": "Description",
"type": "string"
},
"type": {
"default": null,
"title": "Type",
"type": "string"
},
"industry": {
"title": "Industry",
"type": "string"
}
},
"required": [
"title",
"start_date",
"end_date",
"company",
"location",
"description",
"industry"
],
"title": "ResumeWorkExpEntry",
"type": "object"
},
"Row": {
"properties": {
"cells": {
"items": {
"$ref": "#/components/schemas/Cell"
},
"title": "Cells",
"type": "array"
}
},
"title": "Row",
"type": "object"
},
"StateEnum": {
"enum": [
"finished",
"failed",
"Timeout error",
"processing"
],
"type": "string",
"description": "* `finished` - finished\n* `failed` - failed\n* `Timeout error` - Timeout error\n* `processing` - processing"
},
"StatusEnum": {
"enum": [
"sucess",
"fail"
],
"type": "string"
},
"Table": {
"properties": {
"rows": {
"items": {
"$ref": "#/components/schemas/Row"
},
"title": "Rows",
"type": "array"
},
"num_rows": {
"title": "Num Rows",
"type": "integer"
},
"num_cols": {
"title": "Num Cols",
"type": "integer"
}
},
"required": [
"num_rows",
"num_cols"
],
"title": "Table",
"type": "object"
},
"Word": {
"description": "Word of a document\n\nAttributes:\n text (str): Text detected in the word\n bounding_boxes (Sequence[BoundingBox]): Bounding boxes of the words in the word\n confidence (float): Confidence score of the word",
"properties": {
"text": {
"description": "Text detected in the word",
"title": "Text",
"type": "string"
},
"bounding_box": {
"$ref": "#/components/schemas/BoundingBox",
"description": "Bounding boxes of the words in the word"
},
"confidence": {
"description": "Confidence score of the word",
"title": "Confidence",
"type": "integer"
}
},
"required": [
"text",
"bounding_box",
"confidence"
],
"title": "Word",
"type": "object"
},
"asyncocranonymization_asyncResponseModel": {
"properties": {
"results": {
"$ref": "#/components/schemas/ocranonymization_asyncModel"
},
"error": {
"title": "Error",
"type": "string"
},
"public_id": {
"format": "uuid",
"title": "Public Id",
"type": "string"
},
"status": {
"title": "Status",
"type": "string"
}
},
"required": [
"results",
"error",
"public_id",
"status"
],
"title": "asyncocranonymization_asyncResponseModel",
"type": "object"
},
"asyncocrcustom_document_parsing_asyncResponseModel": {
"properties": {
"results": {
"$ref": "#/components/schemas/ocrcustom_document_parsing_asyncModel"
},
"error": {
"title": "Error",
"type": "string"
},
"public_id": {
"format": "uuid",
"title": "Public Id",
"type": "string"
},
"status": {
"title": "Status",
"type": "string"
}
},
"required": [
"results",
"error",
"public_id",
"status"
],
"title": "asyncocrcustom_document_parsing_asyncResponseModel",
"type": "object"
},
"asyncocrocr_asyncResponseModel": {
"properties": {
"results": {
"$ref": "#/components/schemas/ocrocr_asyncModel"
},
"error": {
"title": "Error",
"type": "string"
},
"public_id": {
"format": "uuid",
"title": "Public Id",
"type": "string"
},
"status": {
"title": "Status",
"type": "string"
}
},
"required": [
"results",
"error",
"public_id",
"status"
],
"title": "asyncocrocr_asyncResponseModel",
"type": "object"
},
"asyncocrocr_tables_asyncResponseModel": {
"properties": {
"results": {
"$ref": "#/components/schemas/ocrocr_tables_asyncModel"
},
"error": {
"title": "Error",
"type": "string"
},
"public_id": {
"format": "uuid",
"title": "Public Id",
"type": "string"
},
"status": {
"title": "Status",
"type": "string"
}
},
"required": [
"results",
"error",
"public_id",
"status"
],
"title": "asyncocrocr_tables_asyncResponseModel",
"type": "object"
},
"ocranonymization_asyncAnonymizationAsyncDataClass": {
"properties": {
"document": {
"title": "Document",
"type": "string"
},
"document_url": {
"title": "Document Url",
"type": "string"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"id": {
"title": "Id",
"type": "string"
},
"final_status": {
"allOf": [
{
"$ref": "#/components/schemas/FinalStatusEnum"
}
],
"title": "Final Status"
},
"error": {
"additionalProperties": true,
"default": null,
"title": "Error",
"type": "object"
}
},
"required": [
"document",
"document_url",
"id",
"final_status"
],
"title": "ocranonymization_asyncAnonymizationAsyncDataClass",
"type": "object"
},
"ocranonymization_asyncModel": {
"properties": {
"readyredact": {
"$ref": "#/components/schemas/ocranonymization_asyncAnonymizationAsyncDataClass",
"default": null
},
"base64": {
"$ref": "#/components/schemas/ocranonymization_asyncAnonymizationAsyncDataClass",
"default": null
},
"privateai": {
"$ref": "#/components/schemas/ocranonymization_asyncAnonymizationAsyncDataClass",
"default": null
}
},
"title": "ocranonymization_asyncModel",
"type": "object"
},
"ocrbank_check_parsingBankCheckParsingDataClass": {
"properties": {
"extracted_data": {
"items": {
"$ref": "#/components/schemas/ItemBankCheckParsingDataClass"
},
"title": "Extracted Data",
"type": "array"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"status": {
"allOf": [
{
"$ref": "#/components/schemas/StatusEnum"
}
],
"title": "Status"
}
},
"required": [
"status"
],
"title": "ocrbank_check_parsingBankCheckParsingDataClass",
"type": "object"
},
"ocrbank_check_parsingBankCheckParsingRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"response_as_dict": {
"type": "boolean",
"default": true,
"description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n "
},
"attributes_as_list": {
"type": "boolean",
"default": false,
"description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` "
},
"show_base_64": {
"type": "boolean",
"default": true
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"convert_to_pdf": {
"type": "boolean",
"nullable": true,
"default": false,
"description": "Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers"
}
},
"required": [
"providers"
]
},
"ocrbank_check_parsingResponseModel": {
"properties": {
"mindee": {
"$ref": "#/components/schemas/ocrbank_check_parsingBankCheckParsingDataClass",
"default": null
},
"base64": {
"$ref": "#/components/schemas/ocrbank_check_parsingBankCheckParsingDataClass",
"default": null
},
"veryfi": {
"$ref": "#/components/schemas/ocrbank_check_parsingBankCheckParsingDataClass",
"default": null
},
"extracta": {
"$ref": "#/components/schemas/ocrbank_check_parsingBankCheckParsingDataClass",
"default": null
}
},
"title": "ocrbank_check_parsingResponseModel",
"type": "object"
},
"ocrcustom_document_parsing_asyncCustomDocumentParsingAsyncDataClass": {
"properties": {
"items": {
"items": {
"$ref": "#/components/schemas/CustomDocumentParsingAsyncItem"
},
"title": "Items",
"type": "array"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"id": {
"title": "Id",
"type": "string"
},
"final_status": {
"allOf": [
{
"$ref": "#/components/schemas/FinalStatusEnum"
}
],
"title": "Final Status"
},
"error": {
"additionalProperties": true,
"default": null,
"title": "Error",
"type": "object"
}
},
"required": [
"id",
"final_status"
],
"title": "ocrcustom_document_parsing_asyncCustomDocumentParsingAsyncDataClass",
"type": "object"
},
"ocrcustom_document_parsing_asyncModel": {
"properties": {
"amazon": {
"$ref": "#/components/schemas/ocrcustom_document_parsing_asyncCustomDocumentParsingAsyncDataClass",
"default": null
},
"extracta": {
"$ref": "#/components/schemas/ocrcustom_document_parsing_asyncCustomDocumentParsingAsyncDataClass",
"default": null
}
},
"title": "ocrcustom_document_parsing_asyncModel",
"type": "object"
},
"ocrdata_extractionDataExtractionDataClass": {
"properties": {
"fields": {
"items": {
"$ref": "#/components/schemas/ItemDataExtraction"
},
"title": "Fields",
"type": "array"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"status": {
"allOf": [
{
"$ref": "#/components/schemas/StatusEnum"
}
],
"title": "Status"
}
},
"required": [
"status"
],
"title": "ocrdata_extractionDataExtractionDataClass",
"type": "object"
},
"ocrdata_extractionDataExtractionRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"response_as_dict": {
"type": "boolean",
"default": true,
"description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n "
},
"attributes_as_list": {
"type": "boolean",
"default": false,
"description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` "
},
"show_base_64": {
"type": "boolean",
"default": true
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"convert_to_pdf": {
"type": "boolean",
"nullable": true,
"default": false,
"description": "Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers"
}
},
"required": [
"providers"
]
},
"ocrdata_extractionResponseModel": {
"properties": {
"base64": {
"$ref": "#/components/schemas/ocrdata_extractionDataExtractionDataClass",
"default": null
},
"amazon": {
"$ref": "#/components/schemas/ocrdata_extractionDataExtractionDataClass",
"default": null
}
},
"title": "ocrdata_extractionResponseModel",
"type": "object"
},
"ocrfinancial_parserFinancialParserDataClass": {
"properties": {
"extracted_data": {
"description": "List of parsed financial data objects (per page).",
"items": {
"$ref": "#/components/schemas/FinancialParserObjectDataClass"
},
"title": "Extracted Data",
"type": "array"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"status": {
"allOf": [
{
"$ref": "#/components/schemas/StatusEnum"
}
],
"title": "Status"
}
},
"required": [
"status"
],
"title": "ocrfinancial_parserFinancialParserDataClass",
"type": "object"
},
"ocrfinancial_parserFinancialParserRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"response_as_dict": {
"type": "boolean",
"default": true,
"description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n "
},
"attributes_as_list": {
"type": "boolean",
"default": false,
"description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` "
},
"show_base_64": {
"type": "boolean",
"default": true
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"language": {
"type": "string",
"nullable": true,
"description": "Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))"
},
"document_type": {
"allOf": [
{
"$ref": "#/components/schemas/DocumentTypeEnum"
}
],
"default": "invoice",
"description": "Specify the type of your document. Can be Set to 'auto-detect' for automatic detection if the provider supports it. Otherwise, the default is 'invoice'.\n\n* `auto-detect` - auto-detect\n* `invoice` - invoice\n* `receipt` - receipt"
},
"convert_to_pdf": {
"type": "boolean",
"nullable": true,
"default": false,
"description": "Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers"
}
},
"required": [
"providers"
]
},
"ocrfinancial_parserResponseModel": {
"properties": {
"veryfi": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"tabscanner": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"amazon": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"extracta": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"eagledoc": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"google": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"microsoft": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"mindee": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"openai": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"affinda": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"dataleon": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"klippa": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
},
"base64": {
"$ref": "#/components/schemas/ocrfinancial_parserFinancialParserDataClass",
"default": null
}
},
"title": "ocrfinancial_parserResponseModel",
"type": "object"
},
"ocridentity_parserIdentityParserDataClass": {
"properties": {
"extracted_data": {
"items": {
"$ref": "#/components/schemas/InfosIdentityParserDataClass"
},
"title": "Extracted Data",
"type": "array"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"status": {
"allOf": [
{
"$ref": "#/components/schemas/StatusEnum"
}
],
"title": "Status"
}
},
"required": [
"status"
],
"title": "ocridentity_parserIdentityParserDataClass",
"type": "object"
},
"ocridentity_parserIdentityParserRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"response_as_dict": {
"type": "boolean",
"default": true,
"description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n "
},
"attributes_as_list": {
"type": "boolean",
"default": false,
"description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` "
},
"show_base_64": {
"type": "boolean",
"default": true
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"convert_to_pdf": {
"type": "boolean",
"nullable": true,
"default": false,
"description": "Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers"
}
},
"required": [
"providers"
]
},
"ocridentity_parserResponseModel": {
"properties": {
"amazon": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
},
"microsoft": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
},
"mindee": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
},
"openai": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
},
"affinda": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
},
"klippa": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
},
"base64": {
"$ref": "#/components/schemas/ocridentity_parserIdentityParserDataClass",
"default": null
}
},
"title": "ocridentity_parserResponseModel",
"type": "object"
},
"ocrocrOcrDataClass": {
"properties": {
"text": {
"title": "Text",
"type": "string"
},
"bounding_boxes": {
"items": {
"$ref": "#/components/schemas/Bounding_box"
},
"title": "Bounding Boxes",
"type": "array"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"status": {
"allOf": [
{
"$ref": "#/components/schemas/StatusEnum"
}
],
"title": "Status"
}
},
"required": [
"text",
"status"
],
"title": "ocrocrOcrDataClass",
"type": "object"
},
"ocrocrOcrRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"response_as_dict": {
"type": "boolean",
"default": true,
"description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n "
},
"attributes_as_list": {
"type": "boolean",
"default": false,
"description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` "
},
"show_base_64": {
"type": "boolean",
"default": true
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"language": {
"type": "string",
"nullable": true,
"description": "Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))"
}
},
"required": [
"providers"
]
},
"ocrocrResponseModel": {
"properties": {
"api4ai": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"clarifai": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"amazon": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"microsoft": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"mistral": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"google": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"sentisight": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
},
"base64": {
"$ref": "#/components/schemas/ocrocrOcrDataClass",
"default": null
}
},
"title": "ocrocrResponseModel",
"type": "object"
},
"ocrocr_asyncModel": {
"properties": {
"oneai": {
"$ref": "#/components/schemas/ocrocr_asyncOcrAsyncDataClass",
"default": null
},
"amazon": {
"$ref": "#/components/schemas/ocrocr_asyncOcrAsyncDataClass",
"default": null
},
"microsoft": {
"$ref": "#/components/schemas/ocrocr_asyncOcrAsyncDataClass",
"default": null
},
"mistral": {
"$ref": "#/components/schemas/ocrocr_asyncOcrAsyncDataClass",
"default": null
},
"google": {
"$ref": "#/components/schemas/ocrocr_asyncOcrAsyncDataClass",
"default": null
}
},
"title": "ocrocr_asyncModel",
"type": "object"
},
"ocrocr_asyncOcrAsyncDataClass": {
"properties": {
"raw_text": {
"title": "Raw Text",
"type": "string"
},
"pages": {
"description": "List of pages",
"items": {
"$ref": "#/components/schemas/Page"
},
"title": "Pages",
"type": "array"
},
"number_of_pages": {
"description": "Number of pages in the document",
"title": "Number Of Pages",
"type": "integer"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"id": {
"title": "Id",
"type": "string"
},
"final_status": {
"allOf": [
{
"$ref": "#/components/schemas/FinalStatusEnum"
}
],
"title": "Final Status"
},
"error": {
"additionalProperties": true,
"default": null,
"title": "Error",
"type": "object"
}
},
"required": [
"raw_text",
"number_of_pages",
"id",
"final_status"
],
"title": "ocrocr_asyncOcrAsyncDataClass",
"type": "object"
},
"ocrocr_tables_asyncModel": {
"properties": {
"google": {
"$ref": "#/components/schemas/ocrocr_tables_asyncOcrTablesAsyncDataClass",
"default": null
},
"microsoft": {
"$ref": "#/components/schemas/ocrocr_tables_asyncOcrTablesAsyncDataClass",
"default": null
},
"amazon": {
"$ref": "#/components/schemas/ocrocr_tables_asyncOcrTablesAsyncDataClass",
"default": null
}
},
"title": "ocrocr_tables_asyncModel",
"type": "object"
},
"ocrocr_tables_asyncOcrTablesAsyncDataClass": {
"properties": {
"pages": {
"items": {
"$ref": "#/components/schemas/Page"
},
"title": "Pages",
"type": "array"
},
"num_pages": {
"title": "Num Pages",
"type": "integer"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"id": {
"title": "Id",
"type": "string"
},
"final_status": {
"allOf": [
{
"$ref": "#/components/schemas/FinalStatusEnum"
}
],
"title": "Final Status"
},
"error": {
"additionalProperties": true,
"default": null,
"title": "Error",
"type": "object"
}
},
"required": [
"num_pages",
"id",
"final_status"
],
"title": "ocrocr_tables_asyncOcrTablesAsyncDataClass",
"type": "object"
},
"ocrresume_parserResponseModel": {
"properties": {
"senseloaf": {
"$ref": "#/components/schemas/ocrresume_parserResumeParserDataClass",
"default": null
},
"hireability": {
"$ref": "#/components/schemas/ocrresume_parserResumeParserDataClass",
"default": null
},
"extracta": {
"$ref": "#/components/schemas/ocrresume_parserResumeParserDataClass",
"default": null
},
"openai": {
"$ref": "#/components/schemas/ocrresume_parserResumeParserDataClass",
"default": null
},
"affinda": {
"$ref": "#/components/schemas/ocrresume_parserResumeParserDataClass",
"default": null
},
"klippa": {
"$ref": "#/components/schemas/ocrresume_parserResumeParserDataClass",
"default": null
}
},
"title": "ocrresume_parserResponseModel",
"type": "object"
},
"ocrresume_parserResumeParserDataClass": {
"properties": {
"extracted_data": {
"$ref": "#/components/schemas/ResumeExtractedData"
},
"original_response": {
"default": null,
"description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request",
"title": "Original Response"
},
"status": {
"allOf": [
{
"$ref": "#/components/schemas/StatusEnum"
}
],
"title": "Status"
}
},
"required": [
"extracted_data",
"status"
],
"title": "ocrresume_parserResumeParserDataClass",
"type": "object"
},
"ocrresume_parserResumeParserRequest": {
"type": "object",
"properties": {
"settings": {
"type": "string",
"default": {},
"description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
},
"providers": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"description": "It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)"
},
"fallback_providers": {
"type": "array",
"items": {
"type": "string"
},
"default": [],
"description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ",
"maxItems": 5
},
"response_as_dict": {
"type": "boolean",
"default": true,
"description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n "
},
"attributes_as_list": {
"type": "boolean",
"default": false,
"description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` "
},
"show_base_64": {
"type": "boolean",
"default": true
},
"show_original_response": {
"type": "boolean",
"default": false,
"description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
},
"file": {
"type": "string",
"format": "binary",
"description": "File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**"
},
"file_url": {
"type": "string",
"format": "uri",
"nullable": true,
"description": "File **URL** to analyse to be used with with *content-type*: **application/json**."
},
"file_password": {
"type": "string",
"nullable": true,
"description": "If your PDF file has a password, you can pass it here!",
"maxLength": 200
},
"convert_to_pdf": {
"type": "boolean",
"nullable": true,
"default": false,
"description": "Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers"
}
},
"required": [
"providers"
]
}
},
"securitySchemes": {
"FeatureApiAuth": {
"type": "http",
"scheme": "bearer",
"bearerFormat": "JWT"
}
}
},
"servers": [
{
"url": "https://api.edenai.run/v2"
}
]
}