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Extract individual field from table image to excel with OCR

I have scanned images which have tables as shown in this image:

scanned image with handwritten digits and printed information

I am trying to extract each box separately and perform OCR but when I try to detect horizontal and vertical lines and then detect boxes it's returning the following image:

enter image description here

And when I try to perform other transformations to detect text (erode and dilate) some remains of lines are still coming along with text like below:

dilated text and lines

I cannot detect text only to perform OCR and proper bounding boxes aren't being generated like below:

Image with detected boxes

I cannot get clearly separated boxes using real lines, I've tried this on an image that was edited in paint(as shown below) to add digits and it works.

enter image description here

I don't know which part I'm doing wrong but if there's anything I should try or maybe change/add in my question please please tell me.

#Loading all required libraries 
%pylab inline
import cv2
import numpy as np 
import pandas as pd
import pytesseract
import matplotlib.pyplot as plt
import statistics
from time import sleep
import random

img = cv2.imread('images/scan1.jpg',0)

# for adding border to an image
img1= cv2.copyMakeBorder(img,50,50,50,50,cv2.BORDER_CONSTANT,value=[255,255])

# Thresholding the image
(thresh, th3) = cv2.threshold(img1, 255, 255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)

# to flip image pixel values
th3 = 255-th3

# initialize kernels for table boundaries detections
if(th3.shape[0]<1000):
    ver = np.array([[1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1]])
    hor = np.array([[1,1,1,1,1,1]])

else:
    ver = np.array([[1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1],
               [1]])
    hor = np.array([[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]])




# to detect vertical lines of table borders
img_temp1 = cv2.erode(th3, ver, iterations=3)
verticle_lines_img = cv2.dilate(img_temp1, ver, iterations=3)

# to detect horizontal lines of table borders
img_hor = cv2.erode(th3, hor, iterations=3)
hor_lines_img = cv2.dilate(img_hor, hor, iterations=4)

# adding horizontal and vertical lines
hor_ver = cv2.add(hor_lines_img,verticle_lines_img)

hor_ver = 255-hor_ver

# subtracting table borders from image
temp = cv2.subtract(th3,hor_ver)

temp = 255-temp

#Doing xor operation for erasing table boundaries
tt = cv2.bitwise_xor(img1,temp)

iii = cv2.bitwise_not(tt)

tt1=iii.copy()

#kernel initialization
ver1 = np.array([[1,1],
               [1,1],
               [1,1],
               [1,1],
               [1,1],
               [1,1],
               [1,1],
               [1,1],
               [1,1]])
hor1 = np.array([[1,1,1,1,1,1,1,1,1,1],
               [1,1,1,1,1,1,1,1,1,1]])

#morphological operation
temp1 = cv2.erode(tt1, ver1, iterations=2)
verticle_lines_img1 = cv2.dilate(temp1, ver1, iterations=1)

temp12 = cv2.erode(tt1, hor1, iterations=1)
hor_lines_img2 = cv2.dilate(temp12, hor1, iterations=1)

# doing or operation for detecting only text part and removing rest all
hor_ver = cv2.add(hor_lines_img2,verticle_lines_img1)
dim1 = (hor_ver.shape[1],hor_ver.shape[0])
dim = (hor_ver.shape[1]*2,hor_ver.shape[0]*2)

# resizing image to its double size to increase the text size
resized = cv2.resize(hor_ver, dim, interpolation = cv2.INTER_AREA)

#bitwise not operation for fliping the pixel values so as to apply morphological operation such as dilation and erode
want = cv2.bitwise_not(resized)

if(want.shape[0]<1000):
    kernel1 = np.array([[1,1,1]])
    kernel2 = np.array([[1,1],
                        [1,1]])
    kernel3 = np.array([[1,0,1],[0,1,0],
                       [1,0,1]])
else:
    kernel1 = np.array([[1,1,1,1,1,1]])
    kernel2 = np.array([[1,1,1,1,1],
                        [1,1,1,1,1],
                        [1,1,1,1,1],
                        [1,1,1,1,1]])

tt1 = cv2.dilate(want,kernel1,iterations=2)

# getting image back to its original size
resized1 = cv2.resize(tt1, dim1, interpolation = cv2.INTER_AREA)

# Find contours for image, which will detect all the boxes
contours1, hierarchy1 = cv2.findContours(resized1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

#function to sort contours by its x-axis (top to bottom)
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)


#sorting contours by calling fuction
(cnts, boundingBoxes) = sort_contours(contours1, method="top-to-bottom")

#storing value of all bouding box height
heightlist=[]
for i in range(len(boundingBoxes)):
    heightlist.append(boundingBoxes[i][3])

#sorting height values
heightlist.sort()

sportion = int(.5*len(heightlist))
eportion = int(0.05*len(heightlist))

#taking 50% to 95% values of heights and calculate their mean 
#this will neglect small bounding box which are basically noise 
try:
    medianheight = statistics.mean(heightlist[-sportion:-eportion])
except:
    medianheight = statistics.mean(heightlist[-sportion:-2])

#keeping bounding box which are having height more then 70% of the mean height and deleting all those value where 
# ratio of width to height is less then 0.9
box =[]
imag = iii.copy()
for i in range(len(cnts)):    
    cnt = cnts[i]
    x,y,w,h = cv2.boundingRect(cnt)
    if(h>=.7*medianheight and w/h > 0.9):
        image = cv2.rectangle(imag,(x+4,y-2),(x+w-5,y+h),(0,255,0),1)
        box.append([x,y,w,h])
    # to show image

###Now we have badly detected boxes image as shown
like image 542
dhrumil barot Avatar asked Jan 03 '20 14:01

dhrumil barot


2 Answers

You're on the right track. Here's a continuation of your approach with slight modifications. The idea is:

  1. Obtain binary image. Load image, convert to grayscale, and Otsu's threshold.

  2. Remove all character text contours. We create a rectangular kernel and perform opening to only keep the horizontal/vertical lines. This will effectively make the text into tiny noise so we find contours and filter using contour area to remove them.

  3. Repair horizontal/vertical lines and extract each ROI. We morph close to fix and broken lines and smooth the table. From here we sort the box field contours using imutils.sort_contours() with the top-to-bottom parameter. Next we find contours and filter using contour area then extract each ROI.


Here's a visualization of each box field and the extracted ROI

enter image description here

Code

import cv2
import numpy as np
from imutils import contours

# Load image, grayscale, Otsu's threshold
image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Remove text characters with morph open and contour filtering
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
cnts = cv2.findContours(opening, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 500:
        cv2.drawContours(opening, [c], -1, (0,0,0), -1)

# Repair table lines, sort contours, and extract ROI
close = 255 - cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=1)
cnts = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")
for c in cnts:
    area = cv2.contourArea(c)
    if area < 25000:
        x,y,w,h = cv2.boundingRect(c)
        cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), -1)
        ROI = original[y:y+h, x:x+w]

        # Visualization
        cv2.imshow('image', image)
        cv2.imshow('ROI', ROI)
        cv2.waitKey(20)

cv2.imshow('opening', opening)
cv2.imshow('close', close)
cv2.imshow('image', image)
cv2.waitKey()
like image 168
nathancy Avatar answered Oct 17 '22 01:10

nathancy


nanthancy's answer is also accurate, I used the following script for getting each box and sorting it by columns and rows.

Note: Most of this code is from a medium blog by Kanan Vyas here: https://medium.com/coinmonks/a-box-detection-algorithm-for-any-image-containing-boxes-756c15d7ed26

#most of this code is take from blog by Kanan Vyas here: 
#https://medium.com/coinmonks/a-box-detection-algorithm-for-any-image-containing-boxes-756c15d7ed26


import cv2
import numpy as np

img = cv2.imread('images/scan2.jpg',0)

#fn to show np images with cv2 and close on any key press
def imshow(img, label='default'):
    cv2.imshow(label, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Thresholding the image
(thresh, img_bin) = cv2.threshold(img, 250, 255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)
#inverting the image
img_bin = 255-img_bin 

# Defining a kernel length
kernel_length = np.array(img).shape[1]//80

# A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))# A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))# A kernel of (3 X 3) ones.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

# Morphological operation to detect vertical lines from an image
img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)
verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)
#cv2.imwrite("verticle_lines.jpg",verticle_lines_img)

# Morphological operation to detect horizontal lines from an image
img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)
horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)
#cv2.imwrite("horizontal_lines.jpg",horizontal_lines_img)

# Weighting parameters, this will decide the quantity of an image to be added to make a new image.
alpha = 0.5

beta = 1.0 - alpha# This function helps to add two image with specific weight parameter to get a third image as summation of two image.

img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)

img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)

(thresh, img_final_bin) = cv2.threshold(img_final_bin, 128,255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

cv2.imwrite("img_final_bin.jpg",img_final_bin)


# Find contours for image, which will detect all the boxes
contours, hierarchy = cv2.findContours(img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

""" this section saves each extracted box as a seperate image.
idx = 0
for c in contours:
    # Returns the location and width,height for every contour
    x, y, w, h = cv2.boundingRect(c)
    #only selecting boxes within certain width height range
    if (w > 10 and h > 15 and h < 50):
        idx += 1
        new_img = img[y:y+h, x:x+w]
        #cv2.imwrite("kanan/1/"+ "{}-{}-{}-{}".format(x, y, w, h) + '.jpg', new_img)
"""

#get set of all y-coordinates to sort boxes row wise
def getsety(boxes):
    ally = []
    for b in boxes:
        ally.append(b[1])
    ally = set(ally)
    ally = sorted(ally)
    return ally

#sort boxes by y in certain range, because if image is tilted than same row boxes 
#could have different Ys but within certain range
def sort_boxes(boxes, y, row_column):
    l = []
    for b in boxes:
        if (b[2] > 10 and b[3] > 15 and b[3] < 50):
            if b[1] >= y - 7 and b[1] <= y + 7:
                l.append(b)

    if l in row_column:
        return row_column
    else:
        row_column.append(l)
        return row_column

#sort each row using X of each box to sort it column wise
def sortrows(rc):
    new_rc = []
    for row in rc:
        r_new = sorted(row, key = lambda cell: cell[0])
        new_rc.append(r_new)
    return new_rc

row_column = []
for i in getsety(boundingBoxes):
    row_column = sort_boxes(boundingBoxes, i, row_column)
row_column = [i for i in row_column if i != []]

#final np array with sorted boxes from top left to bottom right
row_column = sortrows(row_column)


I made this in Jupyter notebook and copy-pasted here, if any errors come up, let me know.

Thank you everyone for answers

like image 23
dhrumil barot Avatar answered Oct 17 '22 00:10

dhrumil barot