Extract individual field from table image to excel with OCR

Question

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

I am trying to extract each box separately and perform OCR but when I try to detect horizont

Answer 1

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