Abstract
In this Paper, zone based features are used for recognition of handwritten and printed Kannada and English numerals. The handwritten and printed Kannada and English numeral images are normalized into 32 x 32 dimensions. Then normalized images are divided into 64 zones and their pixel densities are used as feature vector. Thus, the dimension of feature vector is 64. The handwritten and printed Kannada and English numerals are tested for classifications on 4,000 sample images as an experiment and obtained an accuracy of 95.25% for KNN classifier and 97.05% for SVM classifier for mixed numeral inputs with 2-Fold cross validation for handwritten and printed Kannada and English numerals. A total of 40 classes have been reduced to 19 classes pertaining to handwritten and printed Kannada numerals and handwritten and printed English numerals to enable to increase the recognition accuracy. The novelty of the proposed algorithm is thinning free, independent of slant of the characters. General Terms Document Image Analysis
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