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TesseractOcrImageAdaptiveThresholdingSettings Class
Holds settings for aplying the local adaptive binarization method to the image for converting it to black and white.
Inheritance Hierarchy
SystemObject
  DevScope.Ocr.Tesseract.WindowsPhoneTesseractOcrImageAdaptiveThresholdingSettings

Namespace: DevScope.Ocr.Tesseract.WindowsPhone
Assembly: DevScope.Ocr.Tesseract.WindowsPhone (in DevScope.Ocr.Tesseract.WindowsPhone.dll) Version: 255.255.255.255
Syntax
public sealed class TesseractOcrImageAdaptiveThresholdingSettings : __ITesseractOcrImageAdaptiveThresholdingSettingsPublicNonVirtuals

The TesseractOcrImageAdaptiveThresholdingSettings type exposes the following members.

Constructors
  NameDescription
Public methodTesseractOcrImageAdaptiveThresholdingSettings
Initializes a new instance of the TesseractOcrImageAdaptiveThresholdingSettings class.
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Methods
  NameDescription
Public methodEquals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Properties
  NameDescription
Public propertyEnabled
Gets or Sets a value indicating whether adaptive thresholding (a.k.a. local or dynamic thresholding) should be used when thresholding the page image.
Public propertyHalfLocalWindow
Gets or Sets the Local window half size. Must always be greater than 3. The default value is 10.
Public propertyWeightingFactor
Gets or Sets the pixel value intensity Weighting factor. The default value is 0.15.
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Remarks

Thresholding is the simplest way to segment objects from a background.

If that background is relatively uniform, then you should leave the Enabled setting to false (which is the default behaviour) and a global threshold value will be used to binarize the image by pixel-intensity.

If there’s large variation in the background intensity (like a camera image), however, adaptive thresholding (a.k.a. local or dynamic thresholding) may produce better results.

This algorithm uses a custom implementation of the Sauvola method.

See Also