{'fraction_non_alphanumeric': 0.02683982683982684, 'fraction_numerical': 0.0, 'mean_word_length': 5.351648351648351, '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}}
A new approach for multilingual address recognition

In this paper, we proposed, a new method, for multilingual address recognition which is based, on MAP (maximum a posteriori probability) estimation. In the method, an input document image is decomposed to fundamental factors, which are characters, words, text lines and, address areas. These factors are constructed hierarchically in accordance with the degree of their abstraction. As a statistical method is applied, to the formulated relationships among the factors, recognition of a document image is essentially equal to finding the proper position of factors, and classifying each character is attained, through a learning process involving samples of any language type, and thus proper weight coefficients are found, without heuristic management. We constructed, a trial recognition system based on the proposed method. Experimental results show that the proposed system is very promising not only for Japanese envelopes but also for English ones. The proposed method, of address reading is applicable to any type of language, i.e., it is multilingual, by virtue of proper learning from samples.