A neural based segmentation and recognition technique for handwritten words

Artificial neural networks (ANNs) have been successfully applied to optical character recognition (OCR) yielding excellent results. In this paper a technique is presented that segments difficult printed and cursive handwriting, and then classifies the segmented characters. A conventional algorithm is used for the initial segmentation of the words, while an ANN is used to verify whether an accurate segmentation point has been found. After all segmentation points have been detected another ANN is used to identify the characters which remain following the segmentation process. The C programming language, the SP2 supercomputer and a SUN workstation were used for the experiments. The technique has been tested on real-world handwriting scanned from various staff at Griffith University, Gold Coast. Some preliminary experimental results are presented in this paper.

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