do_ocr_multi_class_mlpT_do_ocr_multi_class_mlpDoOcrMultiClassMlpDoOcrMultiClassMlp (Operator)

Name

do_ocr_multi_class_mlpT_do_ocr_multi_class_mlpDoOcrMultiClassMlpDoOcrMultiClassMlp — Classify multiple characters with an OCR classifier.

Signature

do_ocr_multi_class_mlp(Character, Image : : OCRHandle : Class, Confidence)

Description

do_ocr_multi_class_mlpdo_ocr_multi_class_mlpDoOcrMultiClassMlpDoOcrMultiClassMlpDoOcrMultiClassMlp computes the best class for each of the characters given by the regions CharacterCharacterCharacterCharactercharacter and the gray values ImageImageImageImageimage with the OCR classifier OCRHandleOCRHandleOCRHandleOCRHandleOCRHandle and returns the classes in ClassClassClassClassclassVal and the corresponding confidences (probabilities) of the classes in ConfidenceConfidenceConfidenceConfidenceconfidence. In contrast to do_ocr_single_class_mlpdo_ocr_single_class_mlpDoOcrSingleClassMlpDoOcrSingleClassMlpDoOcrSingleClassMlp, do_ocr_multi_class_mlpdo_ocr_multi_class_mlpDoOcrMultiClassMlpDoOcrMultiClassMlpDoOcrMultiClassMlp can classify multiple characters in one call, and therefore typically is faster than a loop that uses do_ocr_single_class_mlpdo_ocr_single_class_mlpDoOcrSingleClassMlpDoOcrSingleClassMlpDoOcrSingleClassMlp to classify single characters. However, do_ocr_multi_class_mlpdo_ocr_multi_class_mlpDoOcrMultiClassMlpDoOcrMultiClassMlpDoOcrMultiClassMlp can only return the best class of each character. Because the confidences can be interpreted as probabilities (see classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlp and evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlpEvaluateClassMlp), and it is therefore easy to check whether a character has been classified with too much uncertainty, this is usually not a disadvantage, except in cases where the classes overlap so much that in many cases the second best class must be examined to be able to decide the class of the character. In these cases, do_ocr_single_class_mlpdo_ocr_single_class_mlpDoOcrSingleClassMlpDoOcrSingleClassMlpDoOcrSingleClassMlp should be used. The result '\x1A'"\x1A""\x1A""\x1A""\x1A" in ClassClassClassClassclassVal signifies that the respective region has been classified as rejection class.

Before calling do_ocr_multi_class_mlpdo_ocr_multi_class_mlpDoOcrMultiClassMlpDoOcrMultiClassMlpDoOcrMultiClassMlp, the classifier must be trained with trainf_ocr_class_mlptrainf_ocr_class_mlpTrainfOcrClassMlpTrainfOcrClassMlpTrainfOcrClassMlp.

Execution Information

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Automatically parallelized on tuple level.

Parameters

CharacterCharacterCharacterCharactercharacter (input_object) region(-array) → object

Characters to be recognized.

ImageImageImageImageimage (input_object) singlechannelimage → object (byte / uint2)

Gray values of the characters.

OCRHandleOCRHandleOCRHandleOCRHandleOCRHandle (input_control) ocr_mlp → (handle)

Handle of the OCR classifier.

ClassClassClassClassclassVal (output_control) string(-array) → (string)

Result of classifying the characters with the MLP.

Number of elements: Class == Character

ConfidenceConfidenceConfidenceConfidenceconfidence (output_control) real(-array) → (real)

Confidence of the class of the characters.

Number of elements: Confidence == Character

Result

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