{'fraction_non_alphanumeric': 0.021484375, 'fraction_numerical': 0.0, 'mean_word_length': 5.029411764705882, '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}}
Using reading models for cursive script recognition

This paper presents a new perception-based model for reading cursive script. We limit the scope of our study to the oo-line recognition of isolated cursive words. Starting from observations and assumptions used in the elaboration of reading models, we describe the organization of our pseudo-neuronal system and show the role of the activation mechanism in perceiving and reading cursive script. We have introduced into our model some characteristics speciic to cursive script. First, we use appropriate features such as ascenders, descenders and loops. Second, we deal with the ambiguity of letter location by introducing the concept of fuzzy position. The location as well as the missing letters are deduced from the context (i.e. the word-letter lexicon). After implementation of our method, preliminary qualitative results have been obtained and are discussed. We are concentrating now on further formalizing and generalizing the proposed model on a larger data base.