2.1 Process and method of identifying OCR
Process the image so that numbers or letters are black on a white background -> create a character recognition handle -> recognize -> clear the handle
Operator: read_ocr_class_mlp( : : FileName : OCRHandle)
示例：read_ocr_class_mlp ('Document_0-9A-Z_NoRej.omc', OCRHandle)
'Document_0-9A-Z_NoRej.omc' (input control parameter): input recognition type
OCRHandle (output control parameter): output recognition handle
算子：do_ocr_multi_class_mlp(Character, Image : : OCRHandle : Class, Confidence)
示例：do_ocr_multi_class_mlp (ObjectSelected, ImageInvert, OCRHandle, Class, Confidence)
ObjectSelected (input object): input recognition area
ImageInvert(input object): input recognition image
OCRHandle (input parameter): input handle
Class (input object): output recognized characters
Confidence (output parameter): the similarity of the output recognition
Operator: comment( : : Comment : )
Example: clear_ocr_class_mlp(OCRHandle)
OCRHandle (input control parameter): input recognition handle
2.2 Process and method of training character recognition library files
1. Extract the characters in the image ->
2. Add each character to the training file (append_ocr_trainf) ->
3. Deduplication (uniq) ->
4. Create a multilayer perceptron (create_ocr_class_mlp) ->
5. Create a new OCR classifier (trainf_ocr_class_mlp) using a multi-layer perceptron ->
6. Write the OCRHandle of the OCR grader to the FileName (file name) of the file (write_ocr_class_mlp) ->
7. Training character file ->
8. Read the trained file to generate the recognized classifier handle read_ocr_class_mlp ->
9. Character recognition: recognize a single character (do_ocr_single_class_mlp), recognize multiple characters (do_ocr_multi_class_mlp) ->
10. Close the classifier handle (clear_ocr_class_mlp)
**************************detailed steps**********************
2. Add each character to the training file (append_ocr_trainf)
算子：append_ocr_trainf(Character, Image : : Class, TrainingFile : )
示例：append_ocr_trainf (ObjectSelected, ImageReduced, tuple[Index], 'train_ocr'+'.trf')
ObjectSelected (input object 1): character Regio
ImageReduced (input object 2): Character Image
Tuple (input control parameter 1): character text
'train_ocr'+'.trf' (input control parameter 2): OCR training .trf file path
Example: chans:=uniq(sort(tuple))
Tuple (input control parameter): charset array
3. Weight removal (uniq)
Example: chans:=uniq(sort(tuple))
Tuple (input control parameter): charset array
4. Create a multilayer perceptron using (create_ocr_class_mlp)
算子：create_ocr_class_mlp( : : WidthCharacter, HeightCharacter, Interpolation, Features, Characters, NumHidden, Preprocessing, NumComponents, RandSeed :OCRHandle)
示例：create_ocr_class_mlp (8, 10, 'constant', 'default', chans, 80, 'none', 10, 42, OCRHandle)
8 (input control parameter 1): character width (default)
10 (input control parameter 2): character width (default)
'constant' (input control parameter 3): character scaling style (default)
'default' (input control parameter 4): features for classification (default)
Chans (input control parameter 5): all characters of the character set to be read
80 (input control parameter 6): number of hidden units for MLP (default)
'none' (input control parameter 7): type of preprocessing used to transform feature vectors (default)
10 (input control parameter 8): preprocessing parameter: the number of transformation features (default)
42 (input control parameter 9): The seed value of the random number generator used to initialize the MLP with random values. (default)
OCRHandle (output control parameter): the handle of the output OCR classifier
5. Create a new OCR classifier using a multilayer perceptron (trainf_ocr_class_mlp)
算子：trainf_ocr_class_mlp( : : OCRHandle, TrainingFile, MaxIterations, WeightTolerance, ErrorTolerance : Error, ErrorLog)
Example: trainf_ocr_class_mlp (OCRHandle, 'train_ocr.trf', 200, 1, 0.01, Error, ErrorLog)
OCRHandle (input control parameter 1): the handle of the OCR classifier
'train_ocr.trf' (input control parameter 2), : training file name
200 (input control parameter 3): the maximum number of iterations of the optimization algorithm (default)
1 (input control parameter 4): Threshold for the difference in MLP weights between two iterations of the optimization algorithm (default)
0.01 (input control parameter 5): The threshold of the average error of the MLP on the training data for two iterations of the optimization algorithm. (default)
Error (input control parameter 6): the average error of the MLP in the training data (default)
ErrorLog (input control parameter 7): The average error of the MLP on the training data as a function of the number of iterations of the optimization algorithm. (default)
6. Write the OCRHandle of the OCR grader to the FileName (file name) of the file
Operator: write_ocr_class_mlp( : : OCRHandle, FileName : )
Example: write_ocr_class_mlp(OCRHandle, 'train_ocr')
OCRHandle (input control parameter 1): the handle of the OCR classifier
'train_ocr' (input control parameter 2): save OCR's .omc classifier to file
7. Training character file
8. Read the trained file to generate the recognized classifier handle read_ocr_class_mlp
Operator: read_ocr_class_mlp( : : FileName : OCRHandle)
Example: read_ocr_class_mlp ('C:/Users/Administrator/Desktop/Learning Folder/Exercise/Exercise 11/train_ocr.omc', OCRHandle1)
'C:/Users/Administrator/Desktop/Learning Folder/Exercise/Exercise 11/train_ocr.omc' (input control parameters): Enter the address of the trained file
OCRHandle1 (output control parameter): the handle of the output OCR classifier
9. Character recognition: recognize a single character (do_ocr_single_class_mlp), recognize multiple characters (do_ocr_multi_class_mlp)
identify a single
算子：do_ocr_single_class_mlp(Character, Image : : OCRHandle, Num : Class, Confidence)
示例：do_ocr_single_class_mlp (SortedRegions1, ImageScaled2, OCRHandle1, 1, Class1, Confidence1)
SortedRegions1 (input object 1): Enter the character region to be recognized
ImageScaled2 (input object 2): Input the image to be recognized
OCRHandle1 (input control parameter 1): the handle of the input OCR classifier
1 (input control parameter 2): Enter the number of best classes to determine (default)
Class1 (input control parameter 3): output the result of classifying characters using MLP (default)
Confidence1 (input control parameter 4): output character recognition score (default)
Identify multiple:
算子：do_ocr_multi_class_mlp(Character, Image : : OCRHandle : Class, Confidence)
示例：do_ocr_multi_class_mlp (ObjectSelected1, ImageScaled2, OCRHandle1, Class, Confidence)
ObjectSelected1 (input object 1): Enter the character area to be recognized
ImageScaled2 (input object 2): Input the image to be recognized
OCRHandle1 (input control parameter 1): the handle of the input OCR classifier
Class (input control parameter 2): Output the result of MLP classifying characters (default)
Confidence (input control parameter 3): output character recognition score (default)
10. Close the classifier handle (clear_ocr_class_mlp)
Operator: clear_ocr_class_mlp( : : OCRHandle : )
Example: clear_ocr_class_mlp (OCRHandle1)
OCRHandle1 (input control parameter): classifier handle