{'fraction_non_alphanumeric': 0.012759170653907496, 'fraction_numerical': 0.001594896331738437, 'mean_word_length': 4.980952380952381, '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}}
New technique in the use of tangent vectors for a robust handwritten digit recognition

This paper proposes the use of tangent vectors for improving handwritten digit recognition accuracy. Basically, tangent vectors and calculated from the training set are combined with mean vectors of numeral classes in order to generate synthetic training data. The performance of the enlarged database is compared to that of the original one using samples of USPS database. An artificial neural network is used to perform the recognition task. The results showed that the use of tangent vectors data, improves the accuracy to more than 2%.