当图像是表格时，是否可以更改图像背景颜色的一部分? [英] Is it possible to change a part of the background color of an image, when the image is a table?

问题描述

我正在使用pytesseract，枕头，cv2对图像进行OCR，并在图像中显示文本.由于我的输入是扫描的PDF文档，因此我首先将其转换为图像(JPEG)格式，然后尝试提取文本.我只有一半.输入为表格，标题未显示，因为标题背景为黑色.我也尝试过getstructuringelement，但无法找出一种方法，这就是我所做的-

I am using pytesseract, pillow,cv2 to OCR an image and get the text present in the image. Since my input is a scanned PDF document, I first converted it into an image (JPEG) format and then tried extracting the text. I am only half way there. The input is a table and the titles are not being displayed, since the titles have a black background. I also tried getstructuringelement but unable to figure out a way Here is what I did-

import cv2
import os  
import numpy as np 
import pytesseract
#import pillow 

#Since scanned PDF can't be handled by pdf2image, convert the scanned PDF into a JPEG format using the below code- 
filename = path   
from pdf2image import convert_from_path 
pages = convert_from_path(filename, 500) for page in pages:
page.save("dest", 'JPEG')


imgname = "path" 
oriimg = cv2.imread(imgname,cv2.IMREAD_COLOR) 
cv2.imshow("original image", oriimg)
cv2.waitKey(0)


#img = cv2.resize(oriimg,None,fx=0.5,fy=0.5,interpolation=cv2.INTER_CUBIC) 
img = cv2.resize(oriimg,(700,1500),interpolation=cv2.INTER_AREA) 
#here length height  
cv2.imshow("lol", img) 
cv2.waitKey(0) 
cv2.imwrite("changed_dimensionsimgpath", img)


import PIL.Image  
image = cv2.imread(imgname,cv2.IMREAD_COLOR) 
grayedimg = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) grayedimg = 
cv2.threshold(grayedimg, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
cv2.imwrite("H://newim.jpg", grayedimg)


pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files (x86)\Tesseract- 
OCR\tesseract.exe"


text = pytesseract.image_to_string(PIL.Image.open("path"))
print(text)

我的输入表如下所示.具有黑色背景的区域不会被OCR识别，也不会被提取为文本. -

My input table looks like below. The regions which have black background are not being identified by OCR and not being extracted as text. --

推荐答案

从图像分析的角度来看，我有3种可能的方式

I have 3 possible ways from an image-analysis perspective

拆分 您可以将图像分为两部分.第一部分只是您的正常流程(加载图像，在其上检测文本).第二步，您首先拍摄图像的底片(255-img)，然后检测文本.

Splitting You can split the images in two part. First part is just your normal flow (load image, detect text on it). The second flow you first take the negative of the image (255 - img) and than detect text.

这两个结果随后需要合并.

The two results will need to be merged afterwards.

差异过滤器 您可以首先应用差异过滤器/边缘检测，这将使具有高对比度的所有内容变高，但是如果做得太极端或某些字母更大，BUT可以改变字母的形状.

difference filter You can first apply a difference filter/edge detection this will high everything with a high contrast BUT can alter the shape of the letters if done to extreme or if some letters are way bigger.

轮廓查找+填充 再次进行边缘检测，但现在非常薄，然后进行轮廓检测.这将以一种颜色重新绘制所有字母.

contour finding + filling Again an edge detection but now very thin and followed with an contour detection. This will redraw all letter in one color.

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