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Compound character recognition of Devanagariscript is one of the challenging tasks since the characters are complexin structure and can be modified by writing combination oftwo or more characters. These compound characters occurs 12 to15% in the Devanagari Script. The moment based techniques arebeing successfully applied to several image processing problemsand represents a fundamental tool to generate feature descriptorswhere the Zernike moment technique has a rotation invarianceproperty which found to be desirable for handwritten characterrecognition. This paper discusses extraction of features fromhandwritten compound characters using Zernike moment featuredescriptor and proposes SVM and k-NN based classification system.The proposed classification system preprocess and normalizethe 27000 handwritten character images into 30x30 pixels imagesand divides them into zones. The pre-classification produces threeclasses depending on presence or absence of vertical bar. FurtherZernike moment feature extraction is performed on each zone.The overall recognition rate of proposed system using SVM andk-NN classifier is upto 98.37%, and 95.82% respectively.

Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition