A better understanding on word classification could lead to a better detection and correction techniques. We used different features or attributes to represent a machine-printed English word, and support vector machines is used to evaluate those features into two class types of word. Our proposed model classifies the words by using fewer words during the training process because those training words are considered personalized words. Our results are very encouraging when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight.
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