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导 读
本文主要介绍Halcon转OpenCV实例--OCR字符识别(附源码)。
实例来源
实例来源于51Halcon论坛的帖子，原贴地址：
https://www.51halcon.com/forum.php?mod=viewthread&tid=889
Halcon实现
测试图：
实现代码与效果：
read_image (Image, 'ET.png')decompose3 (Image, Image1, Image2, Image3)trans_from_rgb(Image1, Image2, Image3, ImageResult1, ImageResult2, ImageResult3, 'hsv')dev_get_window (WindowHandle)draw_rectangle1 (WindowHandle, Row1, Column1, Row2, Column2)gen_rectangle1 (Rectangle, Row1, Column1, Row2, Column2)reduce_domain (ImageResult3, Rectangle, ImageReduced)threshold(ImageReduced, Region, 180, 255)connection (Region, ConnectedRegions)select_shape (ConnectedRegions, SelectedRegions, ['width','height'], 'and', [10,20.24], [50,32.994])
count_obj(SelectedRegions,Number)sort_region (SelectedRegions, SortedRegions1, 'character', 'true', 'column')
invert_image (ImageReduced, ImageInvert)read_ocr_class_mlp ('Industrial_0-9_Rej.omc', OCRHandle)
dev_display (Image)
for Index:=1 to Number by 1 select_obj (SortedRegions1, ObjectSelected, Index) do_ocr_multi_class_mlp (ObjectSelected, ImageInvert, OCRHandle , Class, Confidence) area_center (ObjectSelected, Area, Row, Column) disp_message (WindowHandle, Class, 'image', Row+50, Column, 'black', 'true')
endfor
OpenCV实现步骤与代码
测试图：
说明：由Halcon实现可见，字符识别只做了一部分，这一部分由手动设定ROI来选取，如下图所示：
OTSU阈值分割：
gray = cv2.cvtColor(ROI, cv2.COLOR_BGR2GRAY)_,thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)cv2.imshow("Otsu", thresh)
轮廓提取与筛选：
cnts,_= cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for c in cnts: (x, y, w, h) = cv2.boundingRect(c) if w >= 10 and w <= 50 and h >= 20 and h <= 33:
OCR识别(基于EasyOCR):
result = reader.readtext(char_roi)strText = result[0][1][0]
完整源码：
#公众号：OpenCV与AI深度学习import cv2import numpy as npimport imutilsimport pytesseract #识别失败import easyocrreader = easyocr.Reader(['en'])
image = cv2.imread('ET.png')ROI = image[253:318,86:1440]cv2.imshow('ROI',ROI)
gray = cv2.cvtColor(ROI, cv2.COLOR_BGR2GRAY)_,thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)cv2.imshow("Otsu", thresh)cv2.imwrite('thres.jpg', thresh)
cnts,_= cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for c in cnts: (x, y, w, h) = cv2.boundingRect(c) if w >= 10 and w <= 50 and h >= 20 and h <= 33: char_roi = thresh[y-4:y+h+8,x-4:x+w+8] cv2.imshow('char_roi',char_roi) cv2.waitKey() #text = pytesseract.image_to_string(char_roi, lang='chi_sim', \ # config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789') # 打印识别后的文本 #print(text) result = reader.readtext(char_roi) strText = result[0][1][0] #print(strText) #cv2.drawContours(ROI,c,-1,(0,255,0),3) cv2.rectangle(ROI,(x,y),(x+w,y+h),(0,0,255),1) cv2.putText(image,strText,(86+x,253+y+50), 0, 0.8, (0,255,0), 2) cv2.imshow("chars", ROI)cv2.imshow('result',image)cv2.imwrite('result.jpg',image)
cv2.waitKey()cv2.destroyAllWindows()
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