{'fraction_non_alphanumeric': 0.014063656550703183, 'fraction_numerical': 0.0, 'mean_word_length': 5.407582938388626, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
CHARACTER RECOGNITION WITH OPTIMAL ZONING USING GA

The pattern recognition process is intelligent activity of the human brain system. In the process of recognizing handwritten character, image zoning is a mostly used technique for extracting special characteristics from pattern. Zoning is able to handle variation in handwritten pattern, which is due to different physical and psychological conditions of user. The system is concerned with the handwritten character recognition with optimal zoning described by Verona diagram. In fact, many researchers have proposed several image-zoning topologies according to static and dynamic strategies. But little attention has been paid to the role of feature-zone membership functions which defines how the features influence different zones of the zoning method. The membership functions defined up to now are non adaptive, it follows global approaches that is unable to model local information on feature distributions. In this paper, different zone-based membership functions with adaptive capabilities are defined. The basic idea is to select best suited membership function for each zone of the zoning method. In addition, a genetic algorithm is proposed so that in a unique process the most favourable membership functions and the optimal zoning topology described by Voronoi tessellation is obtained.