{'fraction_non_alphanumeric': 0.022289766970618033, 'fraction_numerical': 0.004052684903748734, 'mean_word_length': 5.72108843537415, '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}}
Sign Language Annotation Algorithm Based on BGRU-CTC

Sign language annotation provides accurate supervised training data for sign language recognition, improves the accuracy of sign language recognition, solving intelligent communication problems between hard of hearing people and listening society. However, the traditional sign language annotation uses manual annotation, which is time-consuming and laborious. Therefore, we propose an automatic sign language annotation algorithm based on Bi-Gated Recurrent Unit(BGRU) and Connectionist Temporal Classification(CTC). This method first extracts the sign language feature sequence through resnet50 and then encodes the sequence through BGRU based on the extracted sign language feature sequence. Learn the relationship between the input sequence and output sequence through CTC. We evaluate the performance of the algorithm on the CSL. The word error rate is 0.3% lower than the state of art methods, which verifies the effectiveness.