{'fraction_non_alphanumeric': 0.02821869488536155, 'fraction_numerical': 0.0, 'mean_word_length': 5.311111111111111, '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}}
Recognizing Hand-Printed Digits with a Distance Quasi-Metric

A distance quasi-metric for pattern recognition is presented. The “quasi” modifier distinguishes the metric from “true” distance metrics which obey a set of standard constraints. By relaxing one of the constraints and coupling it with a fast multidimensional search technique, the metric demonstrates improved accuracy and efficiency compared to other metrics in recognizing hand-written digit samples. A high-level design of a fast optical comparator for computing the distance in O(n) is also presented.