Font identification using Gabor features at sub image level and bin based technique

Abstract

Script identification is an important step in success of multilingual OCR with specialized OCR for each script. Language like Kannada has a wide variety of font style and OCR for Kannada should handle all font type. A multi-OCR with specialized recognizer for each font type is most suitable for Kannada script. Font type identification is a key step in such as solution. We have proposed font identification technique using Gabor features on sub image level. Representatives of Gabor feature are formed and a confidence measure based on Euclidean distance is used as closeness measure. A bin is used which keep track of highest confidence occur at word level and based on maximum bin count font type of a document is identified. Experiments are conducted on scanned Kannada document with 100% as font type identification rate at document level.

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Cite this paper

@article{Urolagin2015FontIU, title={Font identification using Gabor features at sub image level and bin based technique}, author={Siddhaling Urolagin and Anusha Anigol}, journal={2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)}, year={2015}, pages={1-5} }