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This paper presents a hierarchical classification of Optical Character Recognition (OCR) using global and local features with modified k-means and neural network. A hierarchical classification strategy, which contains training and recognition phases, was designed to precisely recognize the characters. In the training phase, the global features of sample characters and modified k-means clustering are employed to quickly categorize the characters into several clusters. Then each cluster and its corresponding local features of sample characters are fed into the Back-Propagation Neural Network (BPN) to learn the optimal weights. In the recognition phase, both global and local features are extracted from the test characters. Then the character is recognized by well-trained clusters’ center and neural network for coarse and fine recognition, respectively. Experimental results of the three different datasets tested showed classification rates of 97.22%, 100% and 90.28%, respectively.

Optical Character Recognition using Global and Local Features with Modified K-means and Neural Network