{'fraction_non_alphanumeric': 0.012698412698412698, 'fraction_numerical': 0.011111111111111112, 'mean_word_length': 5.438775510204081, '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}}
Effective radical segmentation of offline handwritten Chinese characters by using an enhanced snake model and Genetic Algorithm

In this paper, a popular snake model is enhanced by considering the guiding image force and speeded up by incorporating Genetic Algorithm. It has been applied to segment the radicals in offline handwritten Chinese characters. Testing results show that the proposed approach can effectively decompose the radicals with overlaps and connections from the characters with various layout structures. The segmentation accuracy reaches 94.91% and the average running time is around 0.05 second per character.