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InitOcr_MLP_Deprecated

Header:	FIL.h
Namespace:	fil

Creates an OCR multilayer perceptron model.

Syntax

void fil::InitOcr_MLP_Deprecated
(
	const fil::Size& inNormalizationSize,
	ftl::Optional<const ftl::Array<int>& > inHiddenLayerSizes,
	ftl::Optional<int> inRandomSeed,
	fil::MlpPreprocessing::Type inPreprocessing,
	const fil::CharacterFeatures& inCharacterFeatures,
	fil::OcrMlpModel& outOcrModel
)

Parameters

Name	Type	Range	Default	Description
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
inPreprocessing	MlpPreprocessing::Type			Input preprocessing method performed before calculations
inCharacterFeatures	const CharacterFeatures&		(Pixels: True)	Character features used to distinguish characters from each other
outOcrModel	OcrMlpModel&			Result OcrMlpModel used to recognize characters

Description

The filter initializes the MLP classifier and configures its internal structure. As a result, it creates a new OcrMlpModel which must be trained using the TrainOcr_MLP_Deprecated filter.

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_MLP_Deprecated and TrainOcr_MLP_Deprecated will be resized to the size specified in the inNormalizationSize input parameter. The further character classification will be performed on the normalized images. 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 character recognition accuracy is obtained when the size of character is nearly the same as the classifier normalization size.

The character classification depends on character features that are selected in the inCharacterFeatures parameter. At least one feature must be selected. By 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

The table above shows which of the presented character features give normalized results. Learning process of not-normalized data can be a bit slower than learning of normalized data. To reduce this effect the option Normalization should be selected for the input inPreprocessing.

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

List of possible exceptions: