Abstract
Recognition of handwritten Bangla numerals finds numerous applications in postal system automation, passports and document analysis and even for number plate identification. However, the recognition rate requires high and reliable accuracy for practical applications. This paper delineate a robust hybrid system for recognition of handwritten Bangla numerals for the automated postal system, which performed feature extraction using k-means clustering, Baye's theorem and Maximum a Posteriori, then the recognition is performed using Support Vector Machine . Recognition of handwritten numerals, such as postal codes, reveal all kinds of local and global deformations: distortions, different writing styles, thickness variations, wide variety of scales, limited amount of rotation, added noise, occlusion and missing parts. This paper shows that the proposed method is better than other system. Keywords - K-means clustering, Bayes' theorem, MAP, PCA, SVM and OCR.
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