Seeing the character images that an OCR system sees-analysis by genetic algorithm

A new approach to optical character recognition (OCR) is presented. Whereas existing OCR systems have been designed 20 obtain sufficient recognition accuracy, even though this does not provide any direct information useful for improving the systems, this approach uses genetic algorithms to analyze the feature space of the system by visualizing the forms of character images that correspond to the feature vectors in a way that humans can comprehend. It is shown that character images can be reconstructed from feature vectors by 100-generation iteration using genetic algorithms. Experimental results for visualizing reference vectors and category boundaries are presented. The results suggest the existence of ambiguous regions in category boundaries