A table detection, cell recognition and text extraction algorithm to convert tables in images to excel files, using pytesseract and open cv.

cellrecognition.py

import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv

try:
    from PIL import Image
except ImportError:
    import Image
import pytesseract

#read your file
file=r'/Users/marius/Desktop/Masterarbeit/Medium/Medium.png'
img = cv2.imread(file,0)
img.shape

#thresholding the image to a binary image
thresh,img_bin = cv2.threshold(img,128,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)

#inverting the image 
img_bin = 255-img_bin
cv2.imwrite('/Users/marius/Desktop/cv_inverted.png',img_bin)
#Plotting the image to see the output
plotting = plt.imshow(img_bin,cmap='gray')
plt.show()

# countcol(width) of kernel as 100th of total width
kernel_len = np.array(img).shape[1]//100
# Defining a vertical kernel to detect all vertical lines of image 
ver_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_len))
# Defining a horizontal kernel to detect all horizontal lines of image
hor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_len, 1))
# A kernel of 2x2
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))

#Use vertical kernel to detect and save the vertical lines in a jpg
image_1 = cv2.erode(img_bin, ver_kernel, iterations=3)
vertical_lines = cv2.dilate(image_1, ver_kernel, iterations=3)
cv2.imwrite("/Users/marius/Desktop/vertical.jpg",vertical_lines)
#Plot the generated image
plotting = plt.imshow(image_1,cmap='gray')
plt.show()

#Use horizontal kernel to detect and save the horizontal lines in a jpg
image_2 = cv2.erode(img_bin, hor_kernel, iterations=3)
horizontal_lines = cv2.dilate(image_2, hor_kernel, iterations=3)
cv2.imwrite("/Users/marius/Desktop/horizontal.jpg",horizontal_lines)
#Plot the generated image
plotting = plt.imshow(image_2,cmap='gray')
plt.show()

# Combine horizontal and vertical lines in a new third image, with both having same weight.
img_vh = cv2.addWeighted(vertical_lines, 0.5, horizontal_lines, 0.5, 0.0)
#Eroding and thesholding the image
img_vh = cv2.erode(~img_vh, kernel, iterations=2)
thresh, img_vh = cv2.threshold(img_vh,128,255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
cv2.imwrite("/Users/marius/Desktop/img_vh.jpg", img_vh)
bitxor = cv2.bitwise_xor(img,img_vh)
bitnot = cv2.bitwise_not(bitxor)
#Plotting the generated image
plotting = plt.imshow(bitnot,cmap='gray')
plt.show()

# Detect contours for following box detection
contours, hierarchy = cv2.findContours(img_vh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

def sort_contours(cnts, method="left-to-right"):
    # initialize the reverse flag and sort index
    reverse = False
    i = 0
    # handle if we need to sort in reverse
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True
    # handle if we are sorting against the y-coordinate rather than
    # the x-coordinate of the bounding box
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1
    # construct the list of bounding boxes and sort them from top to
    # bottom
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
    key=lambda b:b[1][i], reverse=reverse))
    # return the list of sorted contours and bounding boxes
    return (cnts, boundingBoxes)

# Sort all the contours by top to bottom.
contours, boundingBoxes = sort_contours(contours, method="top-to-bottom")

#Creating a list of heights for all detected boxes
heights = [boundingBoxes[i][3] for i in range(len(boundingBoxes))]

#Get mean of heights
mean = np.mean(heights)

#Create list box to store all boxes in  
box = []
# Get position (x,y), width and height for every contour and show the contour on image
for c in contours:
    x, y, w, h = cv2.boundingRect(c)
    if (w<1000 and h<500):
        image = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
        box.append([x,y,w,h])
        
plotting = plt.imshow(image,cmap='gray')
plt.show()

#Creating two lists to define row and column in which cell is located
row=[]
column=[]
j=0

#Sorting the boxes to their respective row and column
for i in range(len(box)):    
        
    if(i==0):
        column.append(box[i])
        previous=box[i]    
    
    else:
        if(box[i][1]<=previous[1]+mean/2):
            column.append(box[i])
            previous=box[i]            
            
            if(i==len(box)-1):
                row.append(column)        
            
        else:
            row.append(column)
            column=[]
            previous = box[i]
            column.append(box[i])
            
print(column)
print(row)

#calculating maximum number of cells
countcol = 0
for i in range(len(row)):
    countcol = len(row[i])
    if countcol > countcol:
        countcol = countcol

#Retrieving the center of each column
center = [int(row[i][j][0]+row[i][j][2]/2) for j in range(len(row[i])) if row[0]]

center=np.array(center)
center.sort()
print(center)
#Regarding the distance to the columns center, the boxes are arranged in respective order

finalboxes = []
for i in range(len(row)):
    lis=[]
    for k in range(countcol):
        lis.append([])
    for j in range(len(row[i])):
        diff = abs(center-(row[i][j][0]+row[i][j][2]/4))
        minimum = min(diff)
        indexing = list(diff).index(minimum)
        lis[indexing].append(row[i][j])
    finalboxes.append(lis)


#from every single image-based cell/box the strings are extracted via pytesseract and stored in a list
outer=[]
for i in range(len(finalboxes)):
    for j in range(len(finalboxes[i])):
        inner=''
        if(len(finalboxes[i][j])==0):
            outer.append(' ')
        else:
            for k in range(len(finalboxes[i][j])):
                y,x,w,h = finalboxes[i][j][k][0],finalboxes[i][j][k][1], finalboxes[i][j][k][2],finalboxes[i][j][k][3]
                finalimg = bitnot[x:x+h, y:y+w]
                kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 1))
                border = cv2.copyMakeBorder(finalimg,2,2,2,2, cv2.BORDER_CONSTANT,value=[255,255])
                resizing = cv2.resize(border, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
                dilation = cv2.dilate(resizing, kernel,iterations=1)
                erosion = cv2.erode(dilation, kernel,iterations=2)
                
                out = pytesseract.image_to_string(erosion)
                if(len(out)==0):
                    out = pytesseract.image_to_string(erosion, config='--psm 3')
                inner = inner +" "+ out
            outer.append(inner)

#Creating a dataframe of the generated OCR list
arr = np.array(outer)
dataframe = pd.DataFrame(arr.reshape(len(row), countcol))
print(dataframe)
data = dataframe.style.set_properties(align="left")
#Converting it in a excel-file
data.to_excel("/Users/marius/Desktop/output.xlsx")

@huks0: The variable i is a little ambiguous at line 145. It is causing an error.

I ran it on your medium blog image it shows errors.
check here
Amount values are missing. Also, it read the text outside the table.

How can we get the co-ordinates of the cells

Hi,
In 175 lines you have not declared the bitnot because when I run this code it give me an error Undefined bitnot . How solve it or which bitnot images .please tell me.

On line 182 and 184, wouldn't it be better to only OCR just once (just on line 182), by specifying a general configuration, like

pytesseract.image_to_string(erosion, config='-l eng --psm 3')

Then check if the length of the text equals zero and decide whether to append it into the existing variable string?

Cause the already existing solution you've created, perform OCR twice which increases the run time.

Not recognized correctly. cannot extract it

Hi your code worked perfectly thanks. just added a loop to to run through an image folder and append tables to excel file.