trainf_ocr_class_mlp_protectedT_trainf_ocr_class_mlp_protectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedtrainf_ocr_class_mlp_protected (Operator)

Name

trainf_ocr_class_mlp_protectedT_trainf_ocr_class_mlp_protectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedtrainf_ocr_class_mlp_protected — Train an OCR classifier with data from a (protected) training file.

Signature

trainf_ocr_class_mlp_protected( : : OCRHandle, TrainingFile, Password, MaxIterations, WeightTolerance, ErrorTolerance : Error, ErrorLog)

Description

trainf_ocr_class_mlp_protectedtrainf_ocr_class_mlp_protectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedtrainf_ocr_class_mlp_protected trains the OCR classifier OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle with the training data stored in the OCR training files given by TrainingFileTrainingFileTrainingFileTrainingFiletrainingFiletraining_file. Its functionality corresponds to the functionality of trainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlpTrainfOcrClassMlptrainf_ocr_class_mlp, with the addition that trainf_ocr_class_mlp_protectedtrainf_ocr_class_mlp_protectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedtrainf_ocr_class_mlp_protected can process unprotected and protected training files. Protected training files can be used only with the correct user password PasswordPasswordPasswordPasswordpasswordpassword. If the number of passwords PasswordPasswordPasswordPasswordpasswordpassword equals 1, then every input file TrainingFileTrainingFileTrainingFileTrainingFiletrainingFiletraining_file is checked with that password, otherwise the number of passwords has to be equal to the number of input files and the input file at position n is checked with the password at position n. For unprotected training files the passwords are ignored.

For a more detailed description of the operator's functionality see trainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlpTrainfOcrClassMlptrainf_ocr_class_mlp. The concept of protecting OCR training data in HALCON is described in protect_ocr_trainfprotect_ocr_trainfProtectOcrTrainfProtectOcrTrainfProtectOcrTrainfprotect_ocr_trainf.

Execution Information

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Automatically parallelized on internal data level.

This operator modifies the state of the following input parameter:

OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters

OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle (input_control, state is modified) ocr_mlp → (handle)

Handle of the OCR classifier.

TrainingFileTrainingFileTrainingFileTrainingFiletrainingFiletraining_file (input_control) filename.read(-array) → (string)

Names of the training files.

Default value: 'ocr.trf' "ocr.trf" "ocr.trf" "ocr.trf" "ocr.trf" "ocr.trf"

File extension: .trf, .otr

PasswordPasswordPasswordPasswordpasswordpassword (input_control) string(-array) → (string)

Passwords for protected training files.

MaxIterationsMaxIterationsMaxIterationsMaxIterationsmaxIterationsmax_iterations (input_control) integer → (integer)

Maximum number of iterations of the optimization algorithm.

Default value: 200

Suggested values: 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300

WeightToleranceWeightToleranceWeightToleranceWeightToleranceweightToleranceweight_tolerance (input_control) real → (real)

Threshold for the difference of the weights of the MLP between two iterations of the optimization algorithm.

Default value: 1.0

Suggested values: 1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001

Restriction: WeightTolerance >= 1.0e-8

ErrorToleranceErrorToleranceErrorToleranceErrorToleranceerrorToleranceerror_tolerance (input_control) real → (real)

Threshold for the difference of the mean error of the MLP on the training data between two iterations of the optimization algorithm.

Default value: 0.01

Suggested values: 1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001

Restriction: ErrorTolerance >= 1.0e-8

ErrorErrorErrorErrorerrorerror (output_control) real → (real)

Mean error of the MLP on the training data.

ErrorLogErrorLogErrorLogErrorLogerrorLogerror_log (output_control) real-array → (real)

Mean error of the MLP on the training data as a function of the number of iterations of the optimization algorithm.

Result

If the parameters are valid, the operator trainf_ocr_class_mlp_protectedtrainf_ocr_class_mlp_protectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedtrainf_ocr_class_mlp_protected returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

trainf_ocr_class_mlp_protectedtrainf_ocr_class_mlp_protectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedTrainfOcrClassMlpProtectedtrainf_ocr_class_mlp_protected may return the error 9211 (Matrix is not positive definite) if PreprocessingPreprocessingPreprocessingPreprocessingpreprocessingpreprocessing = 'canonical_variates'"canonical_variates""canonical_variates""canonical_variates""canonical_variates""canonical_variates" is used. This typically indicates that not enough training samples have been stored for each class. In this case we recommend to change PreprocessingPreprocessingPreprocessingPreprocessingpreprocessingpreprocessing to 'normalization'"normalization""normalization""normalization""normalization""normalization". Another solution can be to add more training samples.