{'fraction_non_alphanumeric': 0.03215434083601286, 'fraction_numerical': 0.0, 'mean_word_length': 5.144736842105263, '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}}
End-to-End Model Based on Bidirectional LSTM and CTC for Segmentation-free Traditional Mongolian Recognition

A large number of Mongolian books and documents need to be stored and processed by computer. Optical Character Recognition (OCR) can convert a scanned document into text. However, the existing Mongolian OCR systems are realized by a glyph segmentation scheme. However, the glyph segmentation is much more difficult for several fonts. In this study, a segmentation-free approach based on end-to-end model is proposed for traditional Mongolian word recognition. In particular, the proposed model can extract features directly from the input word images and generate the corresponding recognition results (i.e. a sequence of letters). Experimental results demonstrate that the proposed end-to-end model outperforms the segmentation-based method. Moreover, the proposed model can solve the problem of out-of-vocabulary as well.