Abstract

This paper proposes a scheme for recognition of English characters based on features derived from partitioning the character image into non-overlapping cells. A dynamic sliding window moves over each cell and pixel counts obtained from the image portion within the boundaries of the window, contribute towards generation of the feature vector. A total of four passes of the window over the image each with a different window size leads to the generation of a 30-element feature vector. A neural network (multi-layered perceptron) is used for classifying the 26 alphabets of the English language. Accuracies obtained are demonstrated to have been improved upon with respect to contemporary works.

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