{'fraction_non_alphanumeric': 0.020771513353115726, 'fraction_numerical': 0.0, 'mean_word_length': 5.837837837837838, '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}}
A Novel Block Segmentation And Classification Algorithm In Mixed Text/Graphic/Image/Table Documents

The block segmentation and block classification of digitized printed documents segmented into region of texts, graphics, tables and images are very important in document analysis and understanding. Conventionally, the Constrained Run Length Algorithm (CRLA) has been proposed to segment digited document, but failure may occur due to improper constraints. Especially, it usually leads to failure about block segmentation when the documents are complicated and inclined. They could only deal with the text part for block classification without any certain rules, and couldn't succeed in effective classification and even lead to wrong classification. In this paper, a powerful approach for document analysis named "Automatic Local Sequential Segmentation and Hierarchical classification" is proposed. Our results show that this algorithm is an efficient approach for block segmentation and block classification.