Halcon Chinese character recognition

*1 Read images 
read_image (Image, 'C:/Users/Administrator/Desktop/ Character recognition .jpg')
rgb1_to_gray (Image, GrayImage)

*2 Gray linear transformation of image preprocessing 
scale_image (GrayImage, ImageScaled, 2.70, -190)# Make the light brighter , Dark is darker 
*3 Character segmentation 
*3.1 Positioning and geometric affine correction 
threshold (ImageScaled, Regions, 0, 56)
connection (Regions, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 310.76, 3761.47)
************
union1 (SelectedRegions, RegionUnion)# Put four Chinese characters （ Huang,   Luo Chao ） Connect into one 
shape_trans (RegionUnion, RegionTrans, 'rectangle2')# Rectangular transformation with angle 
orientation_region (RegionTrans, Phi)
area_center (RegionTrans, Area, Row, Column)
vector_angle_to_rigid (Row, Column, Phi, Row, Column, rad(180), HomMat2D)
affine_trans_image (ImageScaled, ImageAffineTrans, HomMat2D, 'constant', 'false')
affine_trans_region (RegionUnion, RegionAffineTrans, HomMat2D, 'nearest_neighbor')# The first parameter is zero RegionUnion, Not for RegionTrans, You can remove the middle between the original drawing compilation and Luo Chao “：”
reduce_domain (ImageAffineTrans, RegionAffineTrans, ImageReduced)
*3.2 Character segmentation （ Each word has a connected domain ）
# For the corrected figure “ Huang,   Luo Chao ” Segmentation 
rgb1_to_gray (ImageReduced, GrayImage1)# not essential , It's originally a grayscale image 
threshold (GrayImage1, Regions1, 0, 28)
connection (Regions1, ConnectedRegions1)
count_obj (SortedRegions, Number)
# Check each word （ Just look at , Same as the back ）
for Index := 1 to Number by 1
    select_obj (SortedRegions, ObjectSelected, Index)
endfor

*4 formation trf file , Character image and Character Association 
words:=[' Ed ',' Writing ',' ROM. ',' super ']
TrainFile:='E:/03 CV（ ancient ）/Halcon/ Code '
# Added separately , It can also be added at one time write_ocr_trainf()
# Then manually add various deformed words 
for i := 1 to Number by 1
    select_obj (SortedRegions, SingleWord, i)
    append_ocr_trainf (SingleWord, GrayImage1, words[i-1], TrainFile) # Add characters to the training file   
endfor


read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount)

NumHidden:=20# Didn't work 
*4.1 Create a neural network classifier 
create_ocr_class_mlp (8, 10, 'constant', 'default',  CharacterNames, 80, 'none', 10, 42, OCRHandle)
*4.2 Training classifier 
trainf_ocr_class_mlp (OCRHandle, TrainFile, 200, 1, 0.01, Error, ErrorLog)
*4.3 preservation omc file 
write_ocr_class_mlp (OCRHandle, FontFile)
*4.4 Clear handle 
clear_ocr_class_mlp (OCRHandle)

*5 Identifying text 
read_ocr_class_mlp (FontFile, OCRHandle1)
do_ocr_multi_class_mlp (SortedRegions, GrayImage1, OCRHandle1, Class, Confidence)