{'fraction_non_alphanumeric': 0.03383458646616541, 'fraction_numerical': 0.0, 'mean_word_length': 5.392, '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}}
Table of Contents Recognition using Deep Learning

As far as modern technology is concerned, Optical Character Recognition (OCR) technology is becoming more and more stable. Electronic files are easier to store than paper files, do not take up too much space, and are less likely to suffer from data loss. However, the Document’s Table of Contents (TOC) provides the overall structure of the document, such as chapters and appendices. Therefore, TOC recognition can analyze OCR documents more efficiently. In this study, we developed a novel deep learning method based on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Connectionist Temporal Classification (CTC) to identify TOCs. Finally, the experimental results show that our proposed method outperforms other methods.