Proceeding of International Conference on Computer Science And Engineering ICCSE‐2012

Computational science and engineering is an emerging and promising discipline in shaping future research and development activities in both academia and industry, in fields ranging from engineering, science, finance, and economics, to arts and humanities. New challenges arise in the modelling of complex systems, sophisticated algorithms, advanced scientific and engineering computing and associated (multidisciplinary) problem-solving environments. Because the solution of large and complex problems must cope with tight timing schedules, powerful algorithms and computational techniques, are inevitable.International Conference on Computer Science and Information Technology (CSE) provides a platform for the scientist, computer professionals and students to present their research findings to the international audience. Its objective is to dissemination of original research work on computing science and informatics. It caters to the need of computational scientists, numerical analysts, biologists, engineers, researchers, and graduate students in computing science, informatics and related disciplines.Topic of Interest CSE covers all aspects of computational science and engineering. Topics of interest include, but are not limited to : Scientific and engineering computing, Problem-solving environments, Advanced numerical computation and optimisation, Complex systems: modelling and simulation, Parallel and distributed computing,Architectures and computation models, compiler, hardware and OS issues, so onhttps://www.interscience.in/conf_proc_volumes/1029/thumbnail.jp

A Zone Based Character Recognition Engine for Kannada and English Scripts

AbstractIn this paper, an Optical Character Recognition engine for Kannada and English character recognition is proposed based on zone features. The zone is one of the old concepts in case of document image analysis research. But this method is good in case of Kannada and English character recognition. The total of 2800 Kannada consonants and 2300 English lowercase alphabets sample images are classified based on the SVM classifier. All preprocessed images are normalized into 32 x 32 dimensions, it is optimum. Then the preprocessed image is divided into 64 zones of non overlapping and zone based pixel density is calculated for each of the 64 zones, there by generating 64 features. These features are fed to the SVM classifier for classification of character images. To test the performance of an algorithm 2 fold cross validation is used. The average recognition accuracy of 73.33% and 96.13% is obtained for Kannada consonants and English lowercase alphabets respectively. Further the average percentage of recognition accuracy of 83.02% is obtained for mixture input of both Kannada and English characters. The recognition accuracy obtained for Kannada consonants is low, because most of the characters are similar in shape. Hence, one may need to add some more dominating features to discriminating the characters. In this direction, the work is in progress. It is an initial attempt for mixture of Kannada and English characters recognition with single algorithm. The novelty of the algorithm is independent of thinning and slant of the characters