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python opencv split and recognize table images by table frame lines

python opencv split and recognize table images by table frame lines

Updated October 30, 2019 09:32:32 by HelloWorld!
This article introduces the python opencv will form the picture in accordance with the form of box line segmentation and identification, the text through the sample code is very detailed, for everyone to learn or work with a certain reference value of learning, the need for friends below with the editorial to learn together!

The following small program for the use of python + opencv will be a form of pictures, according to the form of segmentation, and recognition of segmented sub-pictures of the text, I hope that the need for some partners have some help. Specific implementation of the following code.

# -*- coding: utf-8 -*-
"""
Created on Tue May 28 19:23:19 2019
Split image into subimages according to table box line intersections (pass in image path)
@author: hx
"""
 
import cv2
import numpy as np
import pytesseract
 
image = ('C:/Users/Administrator/Desktop/', 1)
# Grayscale images
gray = (image, cv2.COLOR_BGR2GRAY)
# Binarization
binary = (~gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 35, -5)
#ret,binary = (~gray, 127, 255, cv2.THRESH_BINARY)
("Binarized Pictures:", binary) #Show Pictures
(0)
 
rows,cols=
scale = 40
#Identify the horizontal line
kernel = (cv2.MORPH_RECT,(cols//scale,1))
eroded = (binary,kernel,iterations = 1)
#("Eroded Image",eroded)
dilatedcol = (eroded,kernel,iterations = 1)
("Tabular Horizontal Line Presentation:",dilatedcol)
(0)
 
#Identify vertical lines
scale = 20
kernel = (cv2.MORPH_RECT,(1,rows//scale))
eroded = (binary,kernel,iterations = 1)
dilatedrow = (eroded,kernel,iterations = 1)
("Table Vertical Line Display:",dilatedrow)
(0)
 
#Mark the intersection
bitwiseAnd = cv2.bitwise_and(dilatedcol,dilatedrow)
("Table Intersection Display:",bitwiseAnd)
(0)
# ("",bitwiseAnd) #Generate an image from binary pixel points to be saved
 
# Logo Forms
merge = (dilatedcol,dilatedrow)
("Overall presentation of the table:",merge)
(0)
 
 
#Two images subtracted to remove table box lines
merge2 = (binary,merge)
("Pictures to remove table frame line display:",merge2)
(0)
 
#Identify white intersections in black and white plots, take out the horizontal and vertical coordinates
ys,xs = (bitwiseAnd>0)
 
mylisty=[] # vertical coordinate
mylistx=[] # Horizontal coordinates
 
# Get the x and y values of the jumps by sorting them to show that they are intersections, otherwise the intersections would have so many pixel values with similar values that I would just take the last point of the similar values
# This 10 jump is not fixed, depending on the picture will be fine-tuned, basically for the height of the cell form (y-coordinate jump) and length (x-coordinate jump)
i = 0
myxs=(xs)
for i in range(len(myxs)-1):
  if(myxs[i+1]-myxs[i]>10):
    (myxs[i])
  i=i+1
(myxs[i]) #To add the last point
 
 
i = 0
myys=(ys)
#print((ys))
for i in range(len(myys)-1):
  if(myys[i+1]-myys[i]>10):
    (myys[i])
  i=i+1
(myys[i]) #To add the last point
 
print('mylisty',mylisty)
print('mylistx',mylistx)
 
 
# Loop y-coordinate, x-coordinate split table
for i in range(len(mylisty)-1):
  for j in range(len(mylistx)-1):
    # In segmentation, the first parameter is the y-coordinate and the second parameter is the x-coordinate
    ROI = image[mylisty[i]+3:mylisty[i+1]-3,mylistx[j]:mylistx[j+1]-3] The reason for the #minus 3 is because I narrowed down the ROI range
    ("Segmented sub-picture display:",ROI)
    (0)
 
    #special_char_list = '`~!@#$%^&*()-_=+[]{}|\\;:‘'，。《》/？ˇ'
    .tesseract_cmd = 'E:/Tesseract-OCR/'
    text1 = pytesseract.image_to_string(ROI) #Read text, this is the default English
    #text2 = ''.join([char for char in text2 if char not in special_char_list])
    print('Recognize segmented sub-picture information as:'+text1)
    j=j+1
  i=i+1

This is the whole content of this article.

  • python
  • opencv
  • tabular
  • demerger
  • recognize

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