Python 3.x 如何使用OpenCV检测垂直文本以进行提取

我是OpenCV的新手，正在尝试寻找一种方法来检测所附图像的垂直文本。 在第3行的例子中，我希望得到原始成本和以下金额（200000.00美元）的边界框。
类似地，我希望获得现有留置权金额和下面关联金额的边界框。然后，我将使用这些数据发送到OCR引擎以读取文本。传统的OCR引擎逐行提取并丢失上下文。 这是我到目前为止所做的尝试-

import cv2
import numpy as np

img = cv2.imread('Test3.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

edges = cv2.Canny(gray,100,100,apertureSize = 3)
cv2.imshow('edges',edges)
cv2.waitKey(0)

minLineLength = 20
maxLineGap = 10
lines = cv2.HoughLinesP(edges,1,np.pi/180,15,minLineLength=minLineLength,maxLineGap=maxLineGap)

for x in range(0, len(lines)):
    for x1,y1,x2,y2 in lines[x]:
        cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)

cv2.imshow('hough',img)
cv2.waitKey(0)

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我假设边界框是固定的（能够容纳“原始金额”和以下金额的矩形）。您可以使用文本检测来检测“原始金额”和“现有留置权金额”“使用OCR并根据检测到的位置裁剪出图像，以便进一步对金额进行OCR。”。您可以将其用于文本检测

尝试使用图像中的线条将图像分割为不同的单元格

例如，首先通过检测水平线将输入分成行。这可以通过使用

cv.HoughLinesP

并检查每条线的起点和终点的y坐标之间的差值是否小于某个阈值

abs（y2-y1）<10

。如果有一条水平线，它是新行的分隔符。可以使用此行的y坐标水平分割输入

接下来，对于您感兴趣的行，使用相同的技术将区域划分为列，但现在请确保起点和终点的x坐标之间的差值小于某个阈值，因为您现在正在查找垂直线

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现在可以使用水平线的y坐标和垂直线的x坐标将图像裁剪到不同的单元格。将这些裁剪区域逐个传递到OCR引擎，每个单元格都会有相应的文本。

这是我基于和的解决方案

它可能不像你希望的那样“规范”。 但它似乎（或多或少…）与您提供的图像一起工作

请注意：代码在运行它的目录中查找名为“crapped”的文件夹，其中将存储裁剪后的图像。因此，不要在已经包含名为“Cropped”的文件夹的目录中运行它，因为每次运行时它都会删除此文件夹中的所有内容。理解？如果您不确定，请在单独的文件夹中运行它

守则：

# Import required packages 
import cv2 
import numpy as np
import pathlib

###################################################################################################################################
# https://www.pyimagesearch.com/2015/04/20/sorting-contours-using-python-and-opencv/
###################################################################################################################################
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)




###################################################################################################################################
# https://medium.com/coinmonks/a-box-detection-algorithm-for-any-image-containing-boxes-756c15d7ed26    (with a few modifications)
###################################################################################################################################
def box_extraction(img_for_box_extraction_path, cropped_dir_path):
    img = cv2.imread(img_for_box_extraction_path, 0)  # Read the image
    (thresh, img_bin) = cv2.threshold(img, 128, 255,
                                      cv2.THRESH_BINARY | cv2.THRESH_OTSU)  # Thresholding the image
    img_bin = 255-img_bin  # Invert the imagecv2.imwrite("Image_bin.jpg",img_bin)
   
    # Defining a kernel length
    kernel_length = np.array(img).shape[1]//200
     
    # 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 verticle 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)# For Debugging
    # Enable this line to see verticle and horizontal lines in the image which is used to find boxes
    #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)
    # Sort all the contours by top to bottom.
    (contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")
    idx = 0
    for c in contours:
        # Returns the location and width,height for every contour
        x, y, w, h = cv2.boundingRect(c)# If the box height is greater then 20, widht is >80, then only save it as a box in "cropped/" folder.
        if (w > 50 and h > 20):# and w > 3*h:
            idx += 1
            new_img = img[y:y+h, x:x+w]
            cv2.imwrite(cropped_dir_path+str(x)+'_'+str(y) + '.png', new_img)


###########################################################################################################################################################
def prepare_cropped_folder():
   p=pathlib.Path('./Cropped')
   if p.exists():   # Cropped folder non empty. Let's clean up
      files = [x for x in p.glob('*.*') if x.is_file()]
      for f in files:
         f.unlink()
   else:
      p.mkdir()

###########################################################################################################################################################
# MAIN
###########################################################################################################################################################
prepare_cropped_folder()

# Read image from which text needs to be extracted 
img = cv2.imread("dkesg.png") 

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
  
# Performing OTSU threshold 
ret, thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV) 

thresh1=255-thresh1
bin_y=np.zeros(thresh1.shape[0])

for x in range(0,len(bin_y)):
    bin_y[x]=sum(thresh1[x,:])

bin_y=bin_y/max(bin_y)

ry=np.where(bin_y>0.995)[0]

for i in range(0,len(ry)):
   cv2.line(img, (0, ry[i]), (thresh1.shape[1], ry[i]), (0, 0, 0), 1)

# We need to draw abox around the picture with a white border in order for box_detection to work
cv2.line(img,(0,0),(0,img.shape[0]-1),(255,255,255),2)
cv2.line(img,(img.shape[1]-1,0),(img.shape[1]-1,img.shape[0]-1),(255,255,255),2)
cv2.line(img,(0,0),(img.shape[1]-1,0),(255,255,255),2)
cv2.line(img,(0,img.shape[0]-1),(img.shape[1]-1,img.shape[0]-1),(255,255,255),2)

cv2.line(img,(0,0),(0,img.shape[0]-1),(0,0,0),1)
cv2.line(img,(img.shape[1]-3,0),(img.shape[1]-3,img.shape[0]-1),(0,0,0),1)
cv2.line(img,(0,0),(img.shape[1]-1,0),(0,0,0),1)
cv2.line(img,(0,img.shape[0]-2),(img.shape[1]-1,img.shape[0]-2),(0,0,0),1)


cv2.imwrite('out.png',img)
box_extraction("out.png", "./Cropped/")

现在。。。它将裁剪区域放置在裁剪文件夹中。它们被命名为x_y.png，带有（x，y）原始图像上的位置

下面是两个输出示例

及

现在，在一个终点站。我在这两张图片上使用了pytesseract

结果如下：

(一)

原价

200000.00美元

(二)

现有留置权金额

494215.00美元

正如你所看到的，pytesseract在第二个案例中的金额错误。。。所以，要小心

致以最良好的祝愿，

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StpPHAN

欣赏这个上的任何输入？你能认为它每次都在相同的位置，所以你在这个位置席上一个盒子并执行OCR吗？这可能只是一个图像类型的孩子，而典型的OCR的左到右的读在上面的图像中会读“年”-边界框，“原始成本-边界框”。数量现有的留置权-边界框等等。因此，我试图找到一个OpenCV解决方案，可以检测矩形和文本，这样就不会丢失上下文。非常感谢，这是非常有用的。