You are here: Start » AVL.NET » AVL.TrainOcr_SVM(AvlNet.CharacterSample[], AvlNet.Size, float?, float?, float, float, bool, AvlNet.Size?, int?, AvlNet.CharacterFeatures, AvlNet.OcrModel, float, AvlNet.Image[])

AVL.TrainOcr_SVM(AvlNet.CharacterSample[], AvlNet.Size, float?, float?, float, float, bool, AvlNet.Size?, int?, AvlNet.CharacterFeatures, AvlNet.OcrModel, float, AvlNet.Image[])

Trains an OCR support vector machines classifier.

Namespace:	AvlNet
Assembly:	AVL.NET.dll

Syntax


public static void TrainOcr_SVM(
	AvlNet.CharacterSample[] inCharacterSamples,
	AvlNet.Size inNormalizationSize,
	float? inNu,
	float? inKernelGamma,
	float inRegularizationConstant,
	float inStopEpsilon,
	bool inUseShrinkingHeuristics,
	AvlNet.Size? inCharacterSize,
	int? inRandomSeed,
	AvlNet.CharacterFeatures inCharacterFeatures,
	out AvlNet.OcrModel outOcrModel,
	out float outTrainingAccuracy,
	out AvlNet.Image[] diagNormalizedCharacters
)

Parameters

inCharacterSamples: Type: AvlNet.CharacterSample
Training font created from sample regions
inNormalizationSize: Type: AvlNet.Size
The character size after normalization
inNu: Type: System.Nullable<System.Single>
Trade-off between training accuracy and number of supported vectors, or null.
inKernelGamma: Type: System.Nullable<System.Single>
Gamma parameter for RBF kernel, or null.
inRegularizationConstant: Type: System.Single
Preventing overfitting
inStopEpsilon: Type: System.Single
Epsilon for stopping criterion
inUseShrinkingHeuristics: Type: System.Boolean
Heuristics may speed up computations
inCharacterSize: Type: System.Nullable<AvlNet.Size>
Size of fixed width font, or null.
inRandomSeed: Type: System.Nullable<System.Int32>
Random seed used to train classifier, or null.
inCharacterFeatures: Type: AvlNet.CharacterFeatures
Character features used to identify characters
outOcrModel: Type: AvlNet.OcrModel
Trained OcrSvmModel used to recognize characters
outTrainingAccuracy: Type: System.Single
The overall training score
diagNormalizedCharacters: Type: AvlNet.Image
Images of normalized characters used to train classifier

Description

This filter prepares a SVM 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.

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

Remarks

To read more about how to use OCR technique, refer to Machine Vision Guide: Optical Character Recognition

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