{'fraction_non_alphanumeric': 0.016415868673050615, 'fraction_numerical': 0.0, 'mean_word_length': 4.71875, '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}}
Detection of Text in Video

The detection of artificial text in video has been approached in many different ways, but each method shares a common thread. Text must first be segmented, then classified, and finally passed on to another algorithm depending on the overall task. Each method has shown varying degrees of success, however with no common data set and no standard metric for measuring results, comparison is difficult. Some researchers make assumptions about the problem to make it simpler to solve, while others attempt perhaps overly complex methods of accounting for more possibilities. It seems that this task is not impossible to do, but doing it in a way that is robust and elegant does not appear to have been done.