Features for handwriting recognition

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    03-Jan-2016
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Features for handwriting recognition. The challenge. “Rappt JD 10 Feb no 175, om machtiging om af”. Short processing pipeline. Learning. “machtiging”. Feature extraction. Classification. 82,34,66,…. “machtiging”. 0.12. Processing pipeline. Preprocessing. Feature extraction. - PowerPoint PPT Presentation

Transcript of Features for handwriting recognition

  • Features for handwriting recognition

  • | *The challengeRappt JD 10 Feb no 175, om machtiging om af

  • | *Short processing pipelinemachtigingFeature extractionClassification82,34,66,0.12machtigingLearning

  • | *Processing pipelineFeature extractionClassificationPreprocessing

  • | *Input image types

    Color:

    Grayscale:

    Binary:

  • PreprocessingGoal: enhance the foreground while reducing other visual symptoms (stains, noise, pictures, ...)Methods:Contrast stretchingHighpass filteringDespecklingChange color representation (RGB, HSV, grayscale, black/white, )Remove selected connected components () | *

  • | *Connected components

  • | *Processing pipelineSegmentationFeature extractionClassificationPreprocessing

  • | *Object of classificationSentencesWordsCharacters

    (use grammar)(use dictionary)(use alphabet)

  • | *Object representationsImageUnordered vectors (in a coco)Contour vectorsOn-line vectorsSkeleton imageSkeleton vectors

    (x, y)i(x, y)k(x, y)k(x, y)kI(x, y)I(x, y)

  • | *A full processing pipelineSegmentationNormalizationFeature extractionClassificationPreprocessing

  • | *InvarianceLuminance / contrastPositionSizeRotationShearWriter styleInk thickness

  • | *Invariance by normalizationLuminance / contrastPositionSizeRotationShearWriter styleInk thicknessCenter on center of gravityContrast stretchingScale to standard size

  • | *Invariance by trying many deformationsLuminance / contrastPositionSizeRotationShearWriter styleInk thicknessTry different scale factorsTry different rotations and use the best recognition resultTry different deformations

  • | *Invariance by using invariant featuresLuminance / contrastPositionSizeRotationShearWriter styleInk thicknessZernike invariant moments

  • | *A full processing pipelineSegmentationNormalizationFeature extractionClassificationPreprocessing82,34,66,

  • | *Feature ROI typesWhole objectZonesWindowing

  • | *Whole object (wholistic)

  • | *Zones

  • | *Windowing

  • | *Feature typesImage itselfStatisticalStructuralAbstract

    Image (off-line) features (120)Contour / on-line features (21 28)

  • | *Feature 1 3Connected component images

    Scaled image

    Distance transform

    (on whiteboard)

  • | *Feature 4: density histogram

  • | *Feature 5: radon transform

  • | *Feature 6: run count pattern36

  • | *Feature 7: run length patternavgstdev

  • | *Feature 8: Autocorrelation

  • | *Feature 9: Polar zones

  • | *Feature 10: radial zones (tip!)

  • | *Feature 11: zone histograms

  • Feature 12: Hinge | *(By Marius Bulacu)

  • Feature 13: Fraglets | *

  • | *RegelmatighedenSingulariteitenFeature 14: J.C. Simon (1/2)

  • | *"million" ==> convex:concave:3(north:concave) :(north:LOOP):concave:(north:LOOP) :concave:north :concave:HOLE :2(convex:concave)(J.-C. Simon, 1989)Feature 14: J.C. Simon (2/2)

  • | *Feature 15: Structure of background (1/3)

  • | *Feature 15: Structure of background (2/3)

  • | *Feature 15: Structure of background (3/3)

  • | *Feature 16: Structure of foreground + background

  • | *Feature 17: Fourier transform (1/2)From: http://ccp.uchicago.edu/~dcbradle/pages/5.23.06.html

  • | *Feature 17: Fourier transform (2/2)Fig. 1 and 3 from: http://www.csse.uwa.edu.au/~wongt/matlab.htmlFig. 2 from: http://www.chemicool.com/definition/fourier_transform.html

  • | *Feature 18: Wavelet transformFrom: http://www.regonaudio.com/Audio%20Measurement%20via%20Wavelets.html

  • | *Feature 19: Hu invariant momentsarea of the objectcenter of massSlide from: http://www.cedar.buffalo.edu/~govind/CSE717/lectures/CSE717_3.pptInvariant for scale, position and rotationDerived from momentsMoments describe the image distribution with respect to its axes Works on (x, y) vectors

  • | *Feature 20: Zernike momentsFrom: Trier, O. D., Jain, A. K., and Taxt, T. (1996). Feature extraction methods for character recognition - a survey. Pattern Recognition,29:641662.

  • | *Feature 21 28: Contour features(cos, sin) of running angle(cos, sin) of running angular differenceAngular differenceFourier transformInk density (horizontal or vertical)Radon transform: (ink density, computed radially from the c.o.g.)Angular histogramCurvature scale space ()

  • | *Feature 28: Curvature scale spaceFrom: http://www.christine.oppe.info/blog/category/formen-und-farben/formenvergleich/positeration

  • End