HALCON Reference Manual 10.0.2

trainf_ocr_class_mlpT_trainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlp (Operator)

Name

trainf_ocr_class_mlpT_trainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlp — Train an OCR classifier.

Signature

trainf_ocr_class_mlp( : : OCRHandle, TrainingFile, MaxIterations, WeightTolerance, ErrorTolerance : Error, ErrorLog)

Description

trainf_ocr_class_mlptrainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlp trains the OCR classifier OCRHandleOCRHandleOCRHandleOCRHandleOCRHandle with the training characters stored in the OCR training files given by TrainingFileTrainingFileTrainingFileTrainingFiletrainingFile. The training files must have been created, e.g., using write_ocr_trainfwrite_ocr_trainfwrite_ocr_trainfWriteOcrTrainfWriteOcrTrainf, before calling trainf_ocr_class_mlptrainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlp. The remaining parameters have the same meaning as in train_class_mlptrain_class_mlptrain_class_mlpTrainClassMlpTrainClassMlp and are described in detail with train_class_mlptrain_class_mlptrain_class_mlpTrainClassMlpTrainClassMlp. Please, note that training characters that have no corresponding class in the classifier OCRHandleOCRHandleOCRHandleOCRHandleOCRHandle are discarded.

Parallelization

Multithreading type: exclusive (runs in parallel only with independent operators).
Multithreading scope: global (may be called from any thread).
Processed without parallelization.

Parameters

OCRHandleOCRHandleOCRHandleOCRHandleOCRHandle (input_control) ocr_mlp → (integer)

Handle of the OCR classifier.

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

Name(s) of the training file(s).

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

File extension: .trf

MaxIterationsMaxIterationsMaxIterationsMaxIterationsmaxIterations (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

WeightToleranceWeightToleranceWeightToleranceWeightToleranceweightTolerance (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

ErrorToleranceErrorToleranceErrorToleranceErrorToleranceerrorTolerance (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

ErrorErrorErrorErrorerror (output_control) real → (real)

Mean error of the MLP on the training data.

ErrorLogErrorLogErrorLogErrorLogerrorLog (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.

Example (HDevelop)

* Train an OCR classifier
read_ocr_trainf_names ('ocr.trf', CharacterNames, CharacterCount)
create_ocr_class_mlp (8, 10, 'constant', 'default', CharacterNames, 80, \
                      'none', 81, 42, OCRHandle)
trainf_ocr_class_mlp (OCRHandle, 'ocr.trf', 100, 1, 0.01, Error, ErrorLog)
write_ocr_class_mlp (OCRHandle, 'ocr.omc')
clear_ocr_class_mlp (OCRHandle)

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

trainf_ocr_class_mlptrainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlp may return the error 9211 (Matrix is not positive definite) if Preprocessing = '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 Preprocessing to 'normalization'"normalization""normalization""normalization""normalization". Another solution can be to add more training samples.