从表格图像中提取单个字段以使用OCR进行优化 [英] Extract individual field from table image to excel with OCR

问题描述

我已经扫描了包含这张图片所示表格的图片:

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

我试图分别提取每个框并执行OCR，但是当我尝试检测水平和垂直线然后检测框时，它会返回以下图像:

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:

当我尝试执行其他转换以检测文本(侵蚀和膨胀)时，仍然有一些行残留在文本中，如下所示:

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:

我无法仅检测文本来执行OCR，并且未生成如下所示的正确边界框:

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

我无法使用实线获得清晰分隔的框，我已经在用Paint编辑的图像上进行了尝试(如下所示)以添加数字，并且它可以正常工作.

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.

我不知道我做错了什么，但是如果有什么我应该尝试的话，或者可能要更改/添加我的问题，请告诉我.

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

推荐答案

nanthancy的答案也很准确，我使用以下脚本获取每个框并按列和行对其进行排序.

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

注意:大部分代码来自Kanan Vyas的媒体博客，网址为:

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)

我是在Jupyter笔记本上制作的，并在此处复制粘贴，如果出现任何错误，请告诉我.

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

谢谢大家的回答

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