基于python/opencv/tesseract使用传统方法的，表格图片版面分析以及印刷体汉字识别（持续更新，学习备份用）

基于python/opencv/tesseract使用传统方法的，表格图片版面分析以及印刷体汉字识别（持续更新，学习备份用）

import cv2
import numpy as np
import pytesseract

image = cv2.imread('img-625101042_1.jpeg', 1)
print(image.shape)
#二值化
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
binary = cv2.adaptiveThreshold(~gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 35, -5)
#ret,binary = cv2.threshold(~gray, 127, 255, cv2.THRESH_BINARY)
#cv2.imshow("cell", binary)
#cv2.waitKey(0)

rows,cols=binary.shape
scale = 40
#识别横线
kernel  = cv2.getStructuringElement(cv2.MORPH_RECT,(cols//scale,1))
eroded = cv2.erode(binary,kernel,iterations = 1)
#cv2.imshow("Eroded Image",eroded)
dilatedcol = cv2.dilate(eroded,kernel,iterations = 1)
#cv2.imshow("Dilated Image",dilatedcol)
#cv2.waitKey(0)

#识别竖线
scale = 20
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1,rows//scale))
eroded = cv2.erode(binary,kernel,iterations = 1)
dilatedrow = cv2.dilate(eroded,kernel,iterations = 1)
# cv2.imshow("Dilated Image",dilatedrow)
# cv2.waitKey(0)

#标识交点
bitwiseAnd = cv2.bitwise_and(dilatedcol,dilatedrow)
#cv2.imshow("bitwiseAnd Image",bitwiseAnd)
#cv2.waitKey(0)
# cv2.imwrite("my.png",bitwiseAnd)

#标识表格
merge = cv2.add(dilatedcol,dilatedrow)
cv2.imshow("add Image",merge)
cv2.waitKey(0)

#识别黑白图中的白色点
ys,xs = np.where(bitwiseAnd>0)
mylisty=[]
mylistx=[]

#通过排序，获取跳变的x和y的值，说明是交点，否则交点会有好多像素值，我只取最后一点
i = 0
#当两点之间距离大于60时候视为跨越点，交点只留一个像素点
myxs=np.sort(xs)
for i in range(len(myxs)-1):
    if(myxs[i+1]-myxs[i]>60):
        mylistx.append(myxs[i])
    i=i+1
mylistx.append(myxs[i])
# print(mylistx)
# print(len(mylistx))

i = 0
myys=np.sort(ys)
#print(np.sort(ys))
#当两点之间距离大于60时候视为跨越点，交点只留一个像素点
for i in range(len(myys)-1):
    if(myys[i+1]-myys[i]>60):
        mylisty.append(myys[i])
    i=i+1
mylisty.append(myys[i])
# print(mylisty)
# print(len(mylisty))

i=0
contours,hierarchy = cv2.findContours(merge, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
image2 = cv2.drawContours(image, contours, -1, (0,0, 255), 2)
#print(len(contours))
#print(contours[0])
cv2.imshow("image2",image2)
cv2.waitKey(0)

br = []
for i in range(len(contours)):
    BR1= cv2.boundingRect(contours[i])
    br.append(BR1)
    #br = np.array(BR ).reshape()

    print(br)
    #print(x,y,w,h)
    #ROI = image[y:y + h, x:x + w]
    cv2.imshow("roi", ROI)
    cv2.waitKey(0)
    special_char_list = '`~!@#$%^&*()-_=+[]{}|\\;:‘’，。《》/？ˇ'
    pytesseract.pytesseract.tesseract_cmd = 'C://Program Files (x86)/Tesseract-OCR/tesseract.exe'
    text2 = pytesseract.image_to_string(ROI,'chi_sim')  #读取文字，此为默认英文
    text2 = ''.join([char for char in text2 if char not in special_char_list])
    print(text2)



#cv2.rectangle(image,ROI[i][0],ROI[i][1],(0,0,255),2)

#print(ROI)
"""
for i in range(4):  #只有4行有效数字
    ROI = image[mylisty[i]:mylisty[i+1]-3,mylistx[1]:mylistx[2]-3] #减去3的原因是由于我缩小ROI范围
    cv2.imshow("add Image",ROI)
    cv2.waitKey(0)


    i=i+1
"""