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TrainOcr_MLP

Module:	OCR

Trains an OCR multilayer perceptron classifier.

Name	Type	Range	Description
inCharacterSamples	CharacterSample Array		Training font created from sample regions
inNormalizationSize	Size		The character size after normalization
inHiddenLayerSizes	Integer Array *		Internal structure of neuron layers used in classifier
inRandomSeed	Integer *	0 - +	Random seed used by MLP classifier
inCharacterFeatures	CharacterFeatures		Character features used to distinguish characters from each other
inLearningRate	Real	0.01 - 1.0	Suppression level of changes during learning process
inMomentum	Real	0.0 - 1.0	Value of classifier learning momentum
inIterationCount	Integer	1 - +	Learning iteration count
inCharacterSize	Size *		Size of fixed width font
outOcrModel	OcrModel		Trained OcrMlpModel used to recognize characters
outTrainingAccuracy	Real		The overall training score
diagError	Profile		Changes of mean error level progress during learning process
diagNormalizedCharacters	Image Array		Images of normalized characters used to train classifier

Description

This filter prepares a MLP classifier for the further OCR operations.

Filter requires a set of prepared CharacterSample which can be created using MakeCharacterSamples.

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

Examples

Description of usage of this filter can be found in examples and tutorial: Preparing an OCR model.

Remarks

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

Errors

This filter can throw an exception to report error. Read how to deal with errors in Error Handling.

List of possible exceptions:

Error type	Description
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.

Complexity Level

This filter is available on Advanced Complexity Level.

Filter Group

This filter is member of TrainOcr filter group.