trainf_ocr_class_mlp_protected🔗
Short description🔗
trainf_ocr_class_mlp_protected — Train an OCR classifier with data from a (protected) training file.
Signature🔗
trainf_ocr_class_mlp_protected( ocr_mlp OCRHandle, filename.read TrainingFile, string Password, integer MaxIterations, real WeightTolerance, real ErrorTolerance, out real Error, out real ErrorLog )
Description🔗
trainf_ocr_class_mlp_protected trains the OCR classifier
OCRHandle with the training data stored in the OCR
training files given by TrainingFile. Its functionality
corresponds to the functionality of trainf_ocr_class_mlp,
with the addition that trainf_ocr_class_mlp_protected
can process unprotected and protected training files. Protected
training files can be used only with the correct user password
Password. If the number of passwords Password
equals 1, then every input file TrainingFile 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_mlp. The concept of protecting OCR training
data in HALCON is described in protect_ocr_trainf.
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
Password (input_control) string(-array) → (string)
Passwords for protected training files.
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.
Result🔗
If the parameters are valid, the operator
trainf_ocr_class_mlp_protected returns the value 2 (H_MSG_TRUE). If necessary,
an exception is raised.
trainf_ocr_class_mlp_protected 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, protect_ocr_trainf
Possible successors
do_ocr_single_class_mlp, do_ocr_multi_class_mlp, write_ocr_class_mlp
Alternatives
See also
Module🔗
OCR/OCV