| Operators |
trainf_ocr_class_mlp — Train an OCR classifier.
trainf_ocr_class_mlp( : : OCRHandle, TrainingFile, MaxIterations, WeightTolerance, ErrorTolerance : Error, ErrorLog)
trainf_ocr_class_mlp trains the OCR classifier OCRHandle with the training characters stored in the OCR training files given by TrainingFile. The training files must have been created, e.g., using write_ocr_trainf, before calling trainf_ocr_class_mlp.
The remaining parameters have the same meaning as in train_class_mlp and are described in detail with train_class_mlp. A regularization of the OCR classifier and an automatic determination of the regularization parameters (see set_regularization_params_ocr_class_mlp) is taken into account during the training. Furthermore, if a rejection class has been specified using set_rejection_params_ocr_class_mlp, before the actual training the samples for the rejection class are generated.
Please note that training characters that have no corresponding class in the classifier OCRHandle are discarded.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.Handle of the OCR classifier.
Names of the training files.
Default value: 'ocr.trf'
File extension: .trf, .otr
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
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
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
Mean error of the MLP on the training data.
Mean error of the MLP on the training data as a function of the number of iterations of the optimization algorithm.
* 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')
If the parameters are valid, the operator trainf_ocr_class_mlp returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
trainf_ocr_class_mlp may return the error 9211 (Matrix is not positive definite) if Preprocessing = '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'. Another solution can be to add more training samples.
create_ocr_class_mlp, write_ocr_trainf, append_ocr_trainf, write_ocr_trainf_image, set_regularization_params_ocr_class_mlp
do_ocr_single_class_mlp, do_ocr_multi_class_mlp, write_ocr_class_mlp
OCR/OCV
| Operators |