halcon基于mlp神经网络分类器的OCR字符识别

OCR字符识别常用流程如下：

1.读取图像

2.预处理

3.图像分割

4.创建字符标识关联图像区域形成.trf文件

5.创建mlp神经网络分类器create_ocr_class_mlp，然后训练

6.保存.omc文件

7.识别

按照如上的流程，通过一张图实现二十六个字母的训练，在另一张图上实现字母的识别，代码部分包含详细的注释，直接贴上代码如下：

dev_close_window ()*读图read_image (Image, 'C:/Users/Administrator/Desktop/字母/81i58PICZ89.jpg')get_image_size (Image, Width, Height)dev_open_window (0, 0, Width, Height, 'black', WindowHandle)dev_display (Image)*字符分割rgb1_to_gray (Image, GrayImage)threshold (GrayImage, Regions, 50, 200)connection (Regions, ConnectedRegions)sort_region (ConnectedRegions, SortedRegions, 'character', 'true', 'row')count_obj (SortedRegions, Number)*逐个显示确定顺序for Index := 1 to Number by 1    dev_clear_window ()    select_obj (SortedRegions, ObjectSelected, Index)    dev_display (ObjectSelected)    stop ()endfor*字符标识word:= ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']*创建训练文件TrainFile:='D:\\words_A_Z.trf'*将图像区域与字符标识关联，保存到训练文件write_ocr_trainf (SortedRegions, Image, word, TrainFile)*创建OMC文件FontFlie:='D:\\FontA_Z.omc'*读取训练文件read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount)*创建神经网络分类器mlpcreate_ocr_class_mlp (8, 10, 'constant', 'default', CharacterNames, 80, 'none', 10, 42, OCRHandle)*训练trainf_ocr_class_mlp (OCRHandle, TrainFile, 200, 1, 0.01, Error, ErrorLog)*保存训练结果write_ocr_class_mlp (OCRHandle, FontFlie)clear_ocr_class_mlp (OCRHandle)*文字识别dev_close_window ()read_image (Image1, 'C:/Users/Administrator/Desktop/字母/3.jpg')get_image_size (Image1, Width, Height)dev_open_window (0, 0, Width, Height, 'black', WindowHandle)dev_display (Image1)text_line_orientation (ObjectSelected, Image1, 25, -0.523599, 0.523599, OrientationAngle)rotate_image (Image1, ImageRotate, OrientationAngle, 'constant')*字符分割rgb1_to_gray (ImageRotate, GrayImage1)threshold (GrayImage1, Regions2, 0, 55)connection (Regions2, ConnectedRegions1)select_shape (ConnectedRegions1, SelectedRegions, ['area','height'], 'and', [63.54,18.089], [249.07,20])sort_region (SelectedRegions, SortedRegions1, 'character', 'true', 'row')count_obj (SortedRegions1, Number1)*识别read_ocr_class_mlp (FontFlie, OCRHandle1)do_ocr_multi_class_mlp (SortedRegions1, GrayImage1, OCRHandle1, Class, Confidence)dev_display (Image1)for j := 1 to Number1 by 1    select_obj (SortedRegions1, ObjectSelected1, j)    area_center (ObjectSelected1, Area, Row, Column)    disp_message (WindowHandle, Class[j-1], 'window', Row+10, Column, 'black', 'true')    endfor

字符分割

识别结果

源码和图片下载地址：点击打开链接