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We present a novel approach to OCR(Optical Character Recognition) of Koreancharacter, Hangul. As a phonogram, Hangul can represent 11,172 differentcharacters with only 52 graphemes, by describing each character with acombination of the graphemes. As the total number of the characters couldoverwhelm the capacity of a neural network, the existing OCR encoding methodspre-define a smaller set of characters that are frequently used. This designchoice naturally compromises the performance on long-tailed characters in thedistribution. In this work, we demonstrate that grapheme encoding is not onlyefficient but also performant for Hangul OCR. Benchmark tests show that ourapproach resolves two main problems of Hangul OCR: class imbalance and targetclass selection.

Character decomposition to resolve class imbalance problem in Hangul OCR