Question

我正在处理以下多线图像，

我使用下面的python代码按顺序隔离行和字符。但是，我需要识别字间距以重建线条。我尝试了一些像Detecting Interword Space in OCR using Python and OpenCV这样的链接。但是，我无法完成下面显示的代码，

import cv2
import numpy as np
image = cv2.imread("D:\\Users\\Downloads\\CaptureMultiLines.jpg")
cv2.imshow('orig',image)
# image = cv2.resize(image_original,None,fx=4, fy=4, interpolation = cv2.INTER_CUBIC)

#grayscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# original_resized = cv2.resize(gray, (0,0), fx=.2, fy=.2)
# cv2.imshow('gray',gray)
# cv2.waitKey(0)

#Remove Salt and pepper noise
saltpep = cv2.fastNlMeansDenoising(gray,None,9,13)
# original_resized = cv2.resize(saltpep, (0,0), fx=.2, fy=.2)
# cv2.imshow('Grayscale',saltpep)
# cv2.waitKey(0)

#blur
blured = cv2.blur(saltpep,(3,3))
# original_resized = cv2.resize(blured, (0,0), fx=.2, fy=.2)
# cv2.imshow('blured',blured)
# cv2.waitKey(0)

#binary
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)
# original_resized = cv2.resize(thresh, (0,0), fx=.2, fy=.2)
# cv2.imshow('Threshold',thresh)
# cv2.waitKey(0)

#dilation
kernel = np.ones((5,100), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
# original_resized = cv2.resize(img_dilation, (0,0), fx=.2, fy=.2)
cv2.imshow('dilated',img_dilation)
cv2.waitKey(0)

#find contours
im2,ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

#sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[1])

for i, ctr in enumerate(sorted_ctrs):

    # Get bounding box
    x, y, w, h = cv2.boundingRect(ctr)

    # Getting ROI
    roi = image[y:y+h, x:x+w]

# #   show ROI
    cv2.imshow('segment no:' +str(i),roi)
    cv2.waitKey(0)



    im = cv2.resize(roi,None,fx=4, fy=4, interpolation = cv2.INTER_CUBIC)
    ret_1,thresh_1 = cv2.threshold(im,127,255,cv2.THRESH_BINARY_INV)
    # original_resized = cv2.resize(thresh, (0,0), fx=.2, fy=.2)
    cv2.imshow('Threshold_1',thresh_1)
    cv2.waitKey(0)

    kernel = np.ones((10, 20), np.uint8)
    words = cv2.dilate(thresh_1, kernel, iterations=1)
    cv2.imshow('words', words)
    cv2.waitKey(0)


    words=cv2.cvtColor(words, cv2.COLOR_BGR2GRAY);

    #find contours
    im,ctrs_1, hier = cv2.findContours(words, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    #sort contours
    sorted_ctrs_1 = sorted(ctrs_1, key=lambda ctr: cv2.boundingRect(ctr)[0])

    for j, ctr_1 in enumerate(sorted_ctrs_1):

        # Get bounding box
        x_1, y_1, w_1, h_1 = cv2.boundingRect(ctr_1)

        # Getting ROI
        roi_1 = thresh_1[y_1:y_1+h_1, x_1:x_1+w_1]

        # #   show ROI
        cv2.imshow('Line no: ' + str(i) + "word no : " +str(j),roi_1)
        cv2.waitKey(0)



        im = cv2.resize(roi_1,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC)
        ret_2,thresh_2 = cv2.threshold(im,127,255,cv2.THRESH_BINARY_INV)
        # original_resized = cv2.resize(thresh, (0,0), fx=.2, fy=.2)
        cv2.imshow('Threshold_2',thresh_2)
        cv2.waitKey(0)

        ret_2,thresh_2 = cv2.threshold(thresh_1,127,255,cv2.THRESH_BINARY_INV)
        kernel = np.ones((10, 1), np.uint8)
        chars = cv2.dilate(thresh_1, kernel, iterations=1)
        cv2.imshow('chars', chars)
        cv2.waitKey(0)


        chars=cv2.cvtColor(chars, cv2.COLOR_BGR2GRAY);
        #find contours
        im,ctrs_2, hier = cv2.findContours(chars, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        #sort contours
        sorted_ctrs_2 = sorted(ctrs_2, key=lambda ctr: cv2.boundingRect(ctr)[0])

        for z, ctr_2 in enumerate(sorted_ctrs_2):

            # Get bounding box
            x_2, y_2, w_2, h_2 = cv2.boundingRect(ctr_2)

            # Getting ROI
            roi_2 = thresh_2[y_2:y_2 + h_2, x_2:x_2 +w_2]
            # #   show ROI
            cv2.imshow('Line no: ' + str(i) + "Word no : " +str(j) + "Char no : " +str(z),roi_2)
            cv2.waitKey(0)

Answer 1

在以下解决方案中，您可以分别为每个句子获取单词。逐字逐句后，这将按字符输出字符。

这是代码：

import cv2
import numpy as np
image = cv2.imread("test.jpg")
cv2.imshow('orig',image)
# image = cv2.resize(image_original,None,fx=4, fy=4, interpolation = cv2.INTER_CUBIC)

#grayscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# original_resized = cv2.resize(gray, (0,0), fx=.2, fy=.2)
cv2.imshow('gray',gray)
cv2.waitKey(0)

#Remove Salt and pepper noise
saltpep = cv2.fastNlMeansDenoising(gray,None,9,13)
# original_resized = cv2.resize(saltpep, (0,0), fx=.2, fy=.2)
cv2.imshow('Grayscale',saltpep)
cv2.waitKey(0)

#blur
blured = cv2.blur(saltpep,(3,3))
# original_resized = cv2.resize(blured, (0,0), fx=.2, fy=.2)
cv2.imshow('blured',blured)
cv2.waitKey(0)

#binary
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)
# original_resized = cv2.resize(thresh, (0,0), fx=.2, fy=.2)
cv2.imshow('Threshold',thresh)
cv2.waitKey(0)

#dilation
kernel = np.ones((5,100), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
# original_resized = cv2.resize(img_dilation, (0,0), fx=.2, fy=.2)
cv2.imshow('dilated',img_dilation)
cv2.waitKey(0)

#find contours
im2,ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

#sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[1])

for i, ctr in enumerate(sorted_ctrs):

    # Get bounding box
    x, y, w, h = cv2.boundingRect(ctr)

    # Getting ROI
    roi = image[y:y+h, x:x+w]

# #   show ROI
    cv2.imshow('segment no:' +str(i),roi)
    cv2.waitKey(0)



    im = cv2.resize(roi,None,fx=4, fy=4, interpolation = cv2.INTER_CUBIC)
    ret_1,thresh_1 = cv2.threshold(im,127,255,cv2.THRESH_BINARY_INV)
    # original_resized = cv2.resize(thresh, (0,0), fx=.2, fy=.2)
    cv2.imshow('Threshold_1',thresh_1)
    cv2.waitKey(0)

    kernel = np.ones((10, 20), np.uint8)
    words = cv2.dilate(thresh_1, kernel, iterations=1)
    cv2.imshow('words', words)
    cv2.waitKey(0)


    words=cv2.cvtColor(words, cv2.COLOR_BGR2GRAY);

    #find contours
    im,ctrs_1, hier = cv2.findContours(words, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    #sort contours
    sorted_ctrs_1 = sorted(ctrs_1, key=lambda ctr: cv2.boundingRect(ctr)[0])

    for j, ctr_1 in enumerate(sorted_ctrs_1):

        # Get bounding box
        x_1, y_1, w_1, h_1 = cv2.boundingRect(ctr_1)

        # Getting ROI
        roi_1 = thresh_1[y_1:y_1+h_1, x_1:x_1+w_1]

        # #   show ROI
        cv2.imshow('Line no: ' + str(i) + " word no : " +str(j),roi_1)
        cv2.waitKey(0)

        chars = cv2.cvtColor(roi_1, cv2.COLOR_BGR2GRAY);

        # dilation
        kernel = np.ones((10, 1), np.uint8)
        joined = cv2.dilate(chars, kernel, iterations=1)
        # original_resized = cv2.resize(img_dilation, (0,0), fx=.2, fy=.2)
        cv2.imshow('joined', joined)
        cv2.waitKey(0)

        # find contours
        im, ctrs_2, hier = cv2.findContours(joined, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # sort contours
        sorted_ctrs_2 = sorted(ctrs_2, key=lambda ctr: cv2.boundingRect(ctr)[0])



        for k, ctr_2 in enumerate(sorted_ctrs_2):
            # Get bounding box
            x_2, y_2, w_2, h_2 = cv2.boundingRect(ctr_2)

            # Getting ROI
            roi_2 = roi_1[y_2:y_2 + h_2, x_2:x_2 + w_2]

            # #   show ROI
            cv2.imshow('Line no: ' + str(i) + ' word no : ' + str(j) + ' char no: ' + str(k), roi_2)
            cv2.waitKey(0)

在OCR opencv中检测字符之间的空格

1 个答案: