{'fraction_non_alphanumeric': 0.016611295681063124, 'fraction_numerical': 0.006644518272425249, 'mean_word_length': 5.067114093959732, '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}}
Stroke Verification with Gray-level Image for Hangul Video Text Recognition

Traditional OCR uses binarization technique, which makes OCR simple. But it makes strokes ambiguous and that causes recognition errors. Main reason of those errors is similar grapheme pair confusing error. It can be reduced by verifying ambiguous area of gray level image. After checking whether there is similar grapheme pair by analyzing traditional OCR result candidates, the base stroke of confused grapheme can be found using the fitness function which reflects the base stroke characteristics. The possibility of confused stroke existence can be measured by analyzing the boundary area of the base stroke. The result is merged with traditional OCR using score-probability converting. We achieved 68.1% error reduction for target grapheme pair errors by the proposed method and it means that 23.1 % total error is reduced