Trains an OCR multilayer perceptron classifier.
void avl::TrainOcr_MLP ( const atl::Array<avl::CharacterSample>& inCharacterSamples, const avl::Size& inNormalizationSize, atl::Optional<const atl::Array<int>& > inHiddenLayerSizes, atl::Optional<int> inRandomSeed, const avl::CharacterFeatures& inCharacterFeatures, float inLearningRate, float inMomentum, int inIterationCount, atl::Optional<const avl::Size&> inCharacterSize, avl::OcrModel& outOcrModel, float& outTrainingAccuracy, avl::Profile& diagError, atl::Array<avl::Image>& diagNormalizedCharacters )
|inCharacterSamples||const Array<CharacterSample>&||Training font created from sample regions|
|inNormalizationSize||const Size&||(Width: 16, Height: 16)||The character size after normalization|
|inHiddenLayerSizes||Optional<const Array<int>& >||NIL||Internal structure of neuron layers used in classifier|
|inRandomSeed||Optional<int>||0 - +||NIL||Random seed used by MLP classifier|
|inCharacterFeatures||const CharacterFeatures&||(Pixels: True)||Character features used to distinguish characters from each other|
|inLearningRate||float||0.01 - 1.0||0.6f||Suppression level of changes during learning process|
|inMomentum||float||0.0 - 1.0||0.75f||Value of classifier learning momentum|
|inIterationCount||int||1 - +||100||Learning iteration count|
|inCharacterSize||Optional<const Size&>||NIL||Size of fixed width font|
|outOcrModel||OcrModel&||Trained OcrMlpModel used to recognize characters|
|outTrainingAccuracy||float&||The overall training score|
|diagError||Profile&||Changes of mean error level progress during learning process|
|diagNormalizedCharacters||Array<Image>&||Images of normalized characters used to train classifier|
This filter prepares a MLP classifier for the further OCR operations.
Parameter inCharacterSize defines the size of character cropping box. It is especially useful when characters are much bigger than normalization size. When it has Nil value the character is cropped to its bounding box.
Parameters inHiddenLayerSizes, inRandomSeed are used in the process of learning of a newly created MLP classifier. For further parameter description please refer to the documentation of the MLP_Init filter.
All the input regions in filters RecognizeCharacters and TrainOcr_MLP are resized to the size specified in the inNormalizationSize input parameter. The further classification is performed on the normalized regions. Therefore, it is important to select the appropriate normalization size.
The selection of too small normalization size may result in loss of character details. However, too large value of normalization size increases the classifier learning time. The best recognition results are obtained when the size of character is nearly the same as the normalization size.
The character classification depends on character features that are selected in the inCharacterFeatures parameter. At least one feature must be selected. By the default the feature Pixels is selected.
The table below contains the description of each available character feature:
|Feature name||Description||Filter origin||Normalized|
|Pixels||Values of the image pixels after normalization.||False|
|NormalizedPixels||Values of the image pixels after normalization normalized to range <0, 1.0>.||True|
|Convexity||Ratio of the input region area to area of its convex hull.||RegionConvexity||True|
|Circularity||Ratio of the region area to area of its bounding circle.||RegionCircularity||True|
|NumberOfHoles||Number of holes found in the input region.||RegionHoles||True|
|AspectRatio||Ratio of input region width to its height.||RegionBoundingBox||False|
|Width||Region bounding box width.||RegionBoundingBox||False|
|Height||Region bounding box height.||RegionBoundingBox||False|
|AreaRatio||Ratio of the input region area to area of its bounding box.||True|
|DiameterRatio||Ratio of the input region diameter to diameter of its bounding box.||RegionDiameter||True|
|Elongation||Ratio of longer axis of the approximating ellipse to the shorter one.||RegionElongation||False|
|Orientation||Further details in the filter RegionOrientation documentation.||RegionOrientation||True|
|Zoning4x4||Normalized pixel values of region reduced to size 4x4 pixel.||True|
|HorizontalProjection||Values of normalized image projection normalized by region height.||ImageProjection||True|
|VerticalProjection||Values of normalized image projection normalized by region height.||ImageProjection||True|
|HoughCircles||Count of circles found in the normalized image.||True|
|Moment_11||Character geometric moment type M11.||RegionMoment||False|
|Moment_20||Character geometric moment type M20.||RegionMoment||False|
|Moment_02||Character geometric moment type M02.||RegionMoment||False|
It is recommended not to set normalization size greater than 50 pixels in each dimension. That could make learning time too long.
For more remarks about using MLP classifier please refer to the documentation of the MLP_Init filter.
To read more about how to use OCR technique, refer to Machine Vision Guide: Optical Character Recognition
List of possible exceptions:
|DomainError||At least a single feature must be selected in inCharacterFeatures in TrainOcr_MLP.|
|DomainError||Hidden layer should have at least a single hidden layer in TrainOcr_MLP.|
|DomainError||Invalid character sample in TrainOcr_MLP.|
|DomainError||Invalid normalization size in InitOcr_MLP.|