Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition
2014
Compound character recognition of Devanagari
script is one of the challenging tasks since the characters are complex
in structure and can be modified by writing combination of
two or more characters. These compound characters occurs 12 to
15% in the Devanagari Script. The moment based techniques are
being successfully applied to several image processing problems
and represents a fundamental tool to generate feature descriptors
where the Zernike moment technique has a rotation invariance
property which found to be desirable for handwritten character
recognition. This paper discusses extraction of features from
handwritten compound characters using Zernike moment feature
descriptor and proposes SVM and k-NN based classification system.
The proposed classification system preprocess and normalize
the 27000 handwritten character images into 30x30 pixels images
and divides them into zones. The pre-classification produces three
classes depending on presence or absence of vertical bar. Further
Zernike moment feature extraction is performed on each zone.
The overall recognition rate of proposed system using SVM and
k-NN classifier is upto 98.37%, and 95.82% respectively.
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