Abstract
The system presented in this paper uses Optical Character Recognition (OCR) techniques to allow massive capturing of handwritten data from product demand information contained in forms filled out by vendors in their visits to clients. OCR is confronted through feature extraction followed by classification using neighrest neighbor clustering and/or exaustive search (Q-analisis [7]). Details are given on features and both classification methods are compared as far as error rate, speed and memory requirements are concerned.
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