{'fraction_non_alphanumeric': 0.019810508182601206, 'fraction_numerical': 0.002583979328165375, 'mean_word_length': 5.247311827956989, '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}}
Automatic Generation of Statutory Warnings Using Deep Learning

The media conveyed may contain questionable substance, for example, a smoking scene, and related substance, for example, a heavy drinker scene, and so on. These days the movie producers furthermore, editors need to physically check the whole video feed to recognize the frightful substance. After scanning all the scenes, they put enough time and exertion into putting statutory alerts and signs in video substance. Statutory admonitions are shown in public interest declaring that the motion picture/TV indicates does not encourage smoking, drinking liquor and so on. In this paper, we propose a programmed method of statutory admonitions utilizing profound learning framework dependent on one of the profound learning strategy, a Darknet-53 based Convolutional Neural Network which is called YOLOv3. Utilizing this, it is possible to computerize this procedure of including statutory alerts in movies or TV arrangement or videos. For example, if there is a smoking scene in a film, at that point the undertaking will identify it and will demonstrate a related statutory cautioning as a subtitle.