Off-line handwritten arabic word recognition using SVMs with normalized poly kernel
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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This paper proposes a recognition model for English handwritten (lowercase, uppercase and letter) character recognition that uses Freeman chain code (FCC) as the representation technique of an image character. Chain code representation gives the boundary of a character image in which the codes represent the direction of where is the location of the next pixel. An FCC method that uses 8-neighbourhood that starts from direction labelled as 1 to 8 is used. Randomized algorithm is used to generate the FCC. After that, features vector is built. The criteria of features to input the classification is the chain code that converted to 64 features. Support vector machine (SVM) is chosen for the classification step. NIST Databases are used as the data in the experiment. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of English handwritten recognition.