{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook is an aggregation of a single utility used for extracting individual words from the full image.\n",
    "\n",
    "Refer to the \"A* Path Planning Line Segmentation Algorithm\" notebook for detailed code walkthrough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.io import imread\n",
    "from skimage.color import rgb2gray\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.filters import sobel\n",
    "import numpy as np\n",
    "from skimage.filters import threshold_otsu\n",
    "from skimage.util import invert\n",
    "from heapq import *\n",
    "\n",
    "def extract_words_from_image(input_image):\n",
    "    img = rgb2gray(imread(input_image))\n",
    "    \n",
    "    # find the horizontal projection for line segment segregation\n",
    "    sobel_image = sobel(img)\n",
    "    hpp = horizontal_projections(sobel_image)\n",
    "    \n",
    "    #find the midway where we can make a threshold and extract the peaks regions\n",
    "    #divider parameter value is used to threshold the peak values from non peak values.\n",
    "    peaks = find_peak_regions(hpp)\n",
    "    peaks_index = np.array(peaks)[:,0].astype(int)\n",
    "    \n",
    "    segmented_img = np.copy(img)\n",
    "    r,c = segmented_img.shape\n",
    "    for ri in range(r):\n",
    "        if ri in peaks_index:\n",
    "            segmented_img[ri, :] = 0\n",
    "            \n",
    "    hpp_clusters = get_hpp_walking_regions(peaks_index)\n",
    "    \n",
    "    binary_image = get_binary(img)\n",
    "\n",
    "    for cluster_of_interest in hpp_clusters:\n",
    "        nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]\n",
    "        road_blocks = get_road_block_regions(nmap)\n",
    "        road_blocks_cluster_groups = group_the_road_blocks(road_blocks)\n",
    "        #create the doorways\n",
    "        for index, road_blocks in enumerate(road_blocks_cluster_groups):\n",
    "            window_image = nmap[:, road_blocks[0]: road_blocks[1]+10]\n",
    "            binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:][:, road_blocks[0]: road_blocks[1]+10][int(window_image.shape[0]/2),:] *= 0\n",
    "    \n",
    "    #now that everything is cleaner, its time to segment all the lines using the A* algorithm\n",
    "    line_segments = []\n",
    "    for i, cluster_of_interest in enumerate(hpp_clusters):\n",
    "        nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]\n",
    "        path = np.array(astar(nmap, (int(nmap.shape[0]/2), 0), (int(nmap.shape[0]/2),nmap.shape[1]-1)))\n",
    "        offset_from_top = cluster_of_interest[0]\n",
    "        path[:,0] += offset_from_top\n",
    "        line_segments.append(path)\n",
    "\n",
    "    ## add an extra line to the line segments array which represents the last bottom row on the image\n",
    "    last_bottom_row = np.flip(np.column_stack(((np.ones((img.shape[1],))*img.shape[0]), np.arange(img.shape[1]))).astype(int), axis=0)\n",
    "    line_segments.append(last_bottom_row)\n",
    "    \n",
    "    line_images = []\n",
    "    line_count = len(line_segments)\n",
    "    for line_index in range(line_count-1):\n",
    "        line_image = extract_line_from_image(img, line_segments[line_index], line_segments[line_index+1])\n",
    "        line_images.append(line_image)\n",
    "        \n",
    "    # exract words from all lines\n",
    "    line_words = []\n",
    "    for line_image in line_images:\n",
    "        line_words.append(extract_words_from_lines(line_image))\n",
    "     \n",
    "    return line_words\n",
    "\n",
    "def horizontal_projections(image):\n",
    "    return np.sum(image, axis=1)\n",
    "\n",
    "def vertical_projections(image):\n",
    "    return np.sum(image, axis=0)\n",
    "\n",
    "def find_peak_regions(hpp, divider=2):\n",
    "    threshold = (np.max(hpp)-np.min(hpp))/divider\n",
    "    peaks = []\n",
    "    peaks_index = []\n",
    "    for i, hppv in enumerate(hpp):\n",
    "        if hppv < threshold:\n",
    "            peaks.append([i, hppv])\n",
    "    return peaks\n",
    "\n",
    "#group the peaks into walking windows\n",
    "def get_hpp_walking_regions(peaks_index):\n",
    "    hpp_clusters = []\n",
    "    cluster = []\n",
    "    for index, value in enumerate(peaks_index):\n",
    "        cluster.append(value)\n",
    "\n",
    "        if index < len(peaks_index)-1 and peaks_index[index+1] - value > 1:\n",
    "            hpp_clusters.append(cluster)\n",
    "            cluster = []\n",
    "\n",
    "        #get the last cluster\n",
    "        if index == len(peaks_index)-1:\n",
    "            hpp_clusters.append(cluster)\n",
    "            cluster = []\n",
    "            \n",
    "    return hpp_clusters\n",
    "\n",
    "def heuristic(a, b):\n",
    "    return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2\n",
    "\n",
    "#a star path planning algorithm\n",
    "def astar(array, start, goal):\n",
    "\n",
    "    neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]\n",
    "    close_set = set()\n",
    "    came_from = {}\n",
    "    gscore = {start:0}\n",
    "    fscore = {start:heuristic(start, goal)}\n",
    "    oheap = []\n",
    "\n",
    "    heappush(oheap, (fscore[start], start))\n",
    "    \n",
    "    while oheap:\n",
    "\n",
    "        current = heappop(oheap)[1]\n",
    "\n",
    "        if current == goal:\n",
    "            data = []\n",
    "            while current in came_from:\n",
    "                data.append(current)\n",
    "                current = came_from[current]\n",
    "            return data\n",
    "\n",
    "        close_set.add(current)\n",
    "        for i, j in neighbors:\n",
    "            neighbor = current[0] + i, current[1] + j            \n",
    "            tentative_g_score = gscore[current] + heuristic(current, neighbor)\n",
    "            if 0 <= neighbor[0] < array.shape[0]:\n",
    "                if 0 <= neighbor[1] < array.shape[1]:                \n",
    "                    if array[neighbor[0]][neighbor[1]] == 1:\n",
    "                        continue\n",
    "                else:\n",
    "                    # array bound y walls\n",
    "                    continue\n",
    "            else:\n",
    "                # array bound x walls\n",
    "                continue\n",
    "                \n",
    "            if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):\n",
    "                continue\n",
    "                \n",
    "            if  tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1]for i in oheap]:\n",
    "                came_from[neighbor] = current\n",
    "                gscore[neighbor] = tentative_g_score\n",
    "                fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)\n",
    "                heappush(oheap, (fscore[neighbor], neighbor))\n",
    "                \n",
    "    return []\n",
    "\n",
    "def get_binary(img):\n",
    "    mean = np.mean(img)\n",
    "    if mean == 0.0 or mean == 1.0:\n",
    "        return img\n",
    "\n",
    "    thresh = threshold_otsu(img)\n",
    "    binary = img <= thresh\n",
    "    binary = binary*1\n",
    "    return binary\n",
    "\n",
    "def path_exists(window_image):\n",
    "    #very basic check first then proceed to A* check\n",
    "    if 0 in horizontal_projections(window_image):\n",
    "        return True\n",
    "    \n",
    "    padded_window = np.zeros((window_image.shape[0],1))\n",
    "    world_map = np.hstack((padded_window, np.hstack((window_image,padded_window)) ) )\n",
    "    path = np.array(astar(world_map, (int(world_map.shape[0]/2), 0), (int(world_map.shape[0]/2), world_map.shape[1])))\n",
    "    if len(path) > 0:\n",
    "        return True\n",
    "    \n",
    "    return False\n",
    "\n",
    "def get_road_block_regions(nmap):\n",
    "    road_blocks = []\n",
    "    needtobreak = False\n",
    "    \n",
    "    for col in range(nmap.shape[1]):\n",
    "        start = col\n",
    "        end = col+20\n",
    "        if end > nmap.shape[1]-1:\n",
    "            end = nmap.shape[1]-1\n",
    "            needtobreak = True\n",
    "\n",
    "        if path_exists(nmap[:, start:end]) == False:\n",
    "            road_blocks.append(col)\n",
    "\n",
    "        if needtobreak == True:\n",
    "            break\n",
    "            \n",
    "    return road_blocks\n",
    "\n",
    "def group_the_road_blocks(road_blocks):\n",
    "    #group the road blocks\n",
    "    road_blocks_cluster_groups = []\n",
    "    road_blocks_cluster = []\n",
    "    size = len(road_blocks)\n",
    "    for index, value in enumerate(road_blocks):\n",
    "        road_blocks_cluster.append(value)\n",
    "        if index < size-1 and (road_blocks[index+1] - road_blocks[index]) > 1:\n",
    "            road_blocks_cluster_groups.append([road_blocks_cluster[0], road_blocks_cluster[len(road_blocks_cluster)-1]])\n",
    "            road_blocks_cluster = []\n",
    "\n",
    "        if index == size-1 and len(road_blocks_cluster) > 0:\n",
    "            road_blocks_cluster_groups.append([road_blocks_cluster[0], road_blocks_cluster[len(road_blocks_cluster)-1]])\n",
    "            road_blocks_cluster = []\n",
    "\n",
    "    return road_blocks_cluster_groups\n",
    "\n",
    "def extract_line_from_image(image, lower_line, upper_line):\n",
    "    lower_boundary = np.min(lower_line[:, 0])\n",
    "    upper_boundary = np.min(upper_line[:, 0])\n",
    "    img_copy = np.copy(image)\n",
    "    r, c = img_copy.shape\n",
    "    for index in range(c-1):\n",
    "        img_copy[0:lower_line[index, 0], index] = 255\n",
    "        img_copy[upper_line[index, 0]:r, index] = 255\n",
    "    \n",
    "    return img_copy[lower_boundary:upper_boundary, :]\n",
    "\n",
    "def extract_words_from_lines(line_image):\n",
    "    ## now that we have lines, lets segregate them into words.\n",
    "    thresh = threshold_otsu(line_image)\n",
    "    binary = line_image > thresh\n",
    "\n",
    "    # find the vertical projection by adding up the values of all pixels along rows\n",
    "    vertical_projection = vertical_projections(binary)\n",
    "    \n",
    "    height = line_image.shape[0]\n",
    "\n",
    "    ## we will go through the vertical projections and \n",
    "    ## find the sequence of consecutive white spaces in the image\n",
    "    whitespace_lengths = []\n",
    "    whitespace = 0\n",
    "    for vp in vertical_projection:\n",
    "        if vp == height:\n",
    "            whitespace = whitespace + 1\n",
    "        elif vp != height:\n",
    "            if whitespace != 0:\n",
    "                whitespace_lengths.append(whitespace)\n",
    "            whitespace = 0 # reset whitepsace counter. \n",
    "\n",
    "    print(\"whitespaces:\", whitespace_lengths)\n",
    "    avg_white_space_length = np.mean(whitespace_lengths)\n",
    "    print(\"average whitespace lenght:\", avg_white_space_length)\n",
    "    \n",
    "    ## find index of whitespaces which are actually long spaces using the avg_white_space_length\n",
    "    whitespace_length = 0\n",
    "    divider_indexes = []\n",
    "    for index, vp in enumerate(vertical_projection):\n",
    "        if vp == height:\n",
    "            whitespace_length = whitespace_length + 1\n",
    "        elif vp != height:\n",
    "            if whitespace_length != 0 and whitespace_length > avg_white_space_length:\n",
    "                divider_indexes.append(index-int(whitespace_length/2))\n",
    "                whitespace_length = 0 # reset it\n",
    "\n",
    "    print(divider_indexes)\n",
    "    \n",
    "    # lets create the block of words from divider_indexes\n",
    "    divider_indexes = np.array(divider_indexes)\n",
    "    dividers = np.column_stack((divider_indexes[:-1],divider_indexes[1:]))\n",
    "    \n",
    "    words = []\n",
    "    for index, window in enumerate(dividers):\n",
    "        words.append(line_image[:,window[0]:window[1]])\n",
    "        \n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whitespaces: [285, 9, 8, 6, 29, 7, 101, 12, 15, 18, 9, 16, 17, 53, 61, 8, 11, 13]\n",
      "average whitespace lenght: 64.61939853222611\n",
      "[143, 877, 1243, 1487, 1659]\n",
      "whitespaces: [281, 13, 10, 27, 14, 8, 15, 11, 7, 130, 6, 8, 18, 5, 8, 7, 84, 5, 12, 19]\n",
      "average whitespace lenght: 64.02921208323588\n",
      "[141, 685, 1117, 1626]\n",
      "whitespaces: [290, 10, 117, 3, 5, 10, 5, 16, 4, 22, 11, 28, 129, 10, 7, 9, 16, 20, 12, 15, 15, 3, 27]\n",
      "average whitespace lenght: 63.191849394266626\n",
      "[145, 510, 1104, 1418, 1932]\n",
      "whitespaces: [593, 248, 74, 90, 533, 796]\n",
      "average whitespace lenght: 269.7245508538912\n",
      "[297, 760, 1237, 1949]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgH4Eqt1+upRwDgT3tTDwDAzbNarcYYY8zn8zGfzyeeBoC/2PwDcOn+Cn7hD3C9iH8ALt18Ph/7+/sXni0Wi/O/CAAwDfEPwFacnJyMk5OTqccASBP/AFyJN7f8Z2dn4/j4eBwfH9v+A0xI/ANwJVar1fkn/fzyyy/nm//T09OJJwPomm02m6ln4OZzyADg+plNPQDbZ/MPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIGJv6gFImE09AAAANv8AAJAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAETsTT3AnzZTDwAAADfI7G0Pbf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH+ACTx69GjMZrPx9ddfjz/++GPqcQCIEP8AE/jss8/GgwcPxpdffjn29vamHgeACL9xACZwdHQ0Xr9+PT799NOpRwEgxOYfAAAiZpvNZuoZxhjjWgwBsE3L5XJ8+OGHrv0AcBVmb30o/gG25/Xr1+P999+fegwAbr63xr9rPwAAEGHzDwAAN4/NP8BUfJY/ANeBzT8AANw8Nv8AAFAm/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABHiHwAAIsQ/AABE7E09wJ9mUw8AAAA3nc0/AABEiH8AAIgQ/wAAECH+AQAgQvwDAECE+AcAgAjxDwAAEeIfAAAixD8AAESIfwAAiBD/AAAQIf4BACBC/AMAQIT4BwCACPEPAAAR4h8AACLEPwAARIh/AACIEP8AABAh/gEAIEL8AwBAhPgHAIAI8Q8AABH/BTgeV7V0bmU2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_words = extract_words_from_image(\"test4.jpg\")\n",
    "\n",
    "for word_images in line_words:\n",
    "    fig, ax = plt.subplots(figsize=(20,20), nrows=len(word_images))\n",
    "    for index, word in enumerate(word_images):\n",
    "        ax[index].axis(\"off\")\n",
    "        ax[index].imshow(word, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}