trainf_ocr_class_svmT_trainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm (Operator)

Name

trainf_ocr_class_svmT_trainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm — Train an OCR classifier.

Signature

trainf_ocr_class_svm( : : OCRHandle, TrainingFile, Epsilon, TrainMode : )

Description

trainf_ocr_class_svmtrainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm trains the OCR classifier OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle with the training characters stored in the OCR training files given by TrainingFileTrainingFileTrainingFileTrainingFiletrainingFiletraining_file. The training files must have been created, e.g., using write_ocr_trainfwrite_ocr_trainfWriteOcrTrainfWriteOcrTrainfWriteOcrTrainfwrite_ocr_trainf, before calling trainf_ocr_class_svmtrainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm. The parameters EpsilonEpsilonEpsilonEpsilonepsilonepsilon and TrainModeTrainModeTrainModeTrainModetrainModetrain_mode have the same meaning as in train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm. Please, note that training characters that have no corresponding class in the classifier OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle are discarded.

Execution Information

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

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_svm → (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

EpsilonEpsilonEpsilonEpsilonepsilonepsilon (input_control) real → (real)

Stop parameter for training.

Default value: 0.001

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

TrainModeTrainModeTrainModeTrainModetrainModetrain_mode (input_control) number → (string / integer)

Mode of training.

Default value: 'default' "default" "default" "default" "default" "default"

List of values: 'add_sv_to_train_set'"add_sv_to_train_set""add_sv_to_train_set""add_sv_to_train_set""add_sv_to_train_set""add_sv_to_train_set", 'default'"default""default""default""default""default"

Example (HDevelop)

* Train an OCR classifier
read_ocr_trainf_names ('ocr.trf', CharacterNames, CharacterCount)
create_ocr_class_svm (8, 10, 'constant', 'default', CharacterNames, \
                      'rbf', 0.01, 0.01, 'one-versus-one', \
                      'normalization', 81, OCRHandle)
trainf_ocr_class_svm (OCRHandle, 'ocr.trf', 0.001, 'default')
write_ocr_class_svm (OCRHandle, 'ocr.osc')

Result

If the parameters are valid the operator trainf_ocr_class_svmtrainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm returns the value TRUE. If necessary, an exception is raised.

trainf_ocr_class_svmtrainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm 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.