{'fraction_non_alphanumeric': 0.02692664809656453, 'fraction_numerical': 0.005571030640668524, 'mean_word_length': 5.304093567251462, '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}}
Combination of Hough Transform and Neural Network on recognizing mathematical symbols

Offline printed mathematical symbol recognition is a particularly difficult task. Recognizing mathematical symbols is one stage within the overall system for recognition of mathematical documents. We describe many experiments using MultiLayer Perceptron (MLP), Hough Transform (HT), k Nearest Neighbors (kNN) and structural Freeman chain code, to enhance symbol recognition of printed mathematics. First, we investigate the use of a MLP based method. Second, we compare the performance of a proposal neural structural method, named HT-MLPs, on symbols that initial MLP usually confuses. The inclusion of HT in MLP reduces symbol confusion rate by 21% and improves recognition rates from 72% to 93%. To improve the efficiency of the proposed method, we compare it to KNN then to Freeman code based methods, commonly used in pattern recognition. While analyzing results, we show that HT-MLP always gives a lower mean confusion and rejection and higher success rates than the others solutions.