trainf_ocr_class_mlp🔗
Short description🔗
trainf_ocr_class_mlp — Train an OCR classifier.
Signature🔗
trainf_ocr_class_mlp( ocr_mlp OCRHandle, filename.read TrainingFile, integer MaxIterations, real WeightTolerance, real ErrorTolerance, out real Error, out real ErrorLog )
Description🔗
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.
Execution information🔗
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:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
Parameters🔗
OCRHandle (input_control, state is modified) ocr_mlp → (handle)
Handle of the OCR classifier.
TrainingFile (input_control) filename.read(-array) → (string)
Names of the training files.
Default: 'ocr.trf'
File extension: .trf, .otr
MaxIterations (input_control) integer → (integer)
Maximum number of iterations of the optimization algorithm.
Default: 200
Suggested values: 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300
WeightTolerance (input_control) real → (real)
Threshold for the difference of the weights of the MLP between two iterations of the optimization algorithm.
Default: 1.0
Suggested values: 1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001
Restriction: WeightTolerance >= 1.0e-8
ErrorTolerance (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: 0.01
Suggested values: 1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001
Restriction: ErrorTolerance >= 1.0e-8
Error (output_control) real → (real)
Mean error of the MLP on the training data.
ErrorLog (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')
Result🔗
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.
Combinations with other operators🔗
Combinations
Possible predecessors
create_ocr_class_mlp, write_ocr_trainf, append_ocr_trainf, write_ocr_trainf_image, set_regularization_params_ocr_class_mlp
Possible successors
do_ocr_single_class_mlp, do_ocr_multi_class_mlp, write_ocr_class_mlp
Alternatives
See also
Module🔗
OCR/OCV