Optical Character Recognition with Chinese and Korean Character Decomposition

We present our work on Optical Character Recognition on Chinese and Korean Characters for line level transcriptions. One challenge for recognizing Chinese and Korean is that there are thousands of characters for a system to recognize. In addition, many uncommon characters only appear a couple of times in training. We use character decomposition methods to break characters into smaller constituent graphemes. CangJie is used for Chinese character decomposition and Korean Jamo is used for Korean character decomposition. Character decomposition reduces the size of the Neural Network models and allows training examples to be shared across uncommon characters with the same graphemes. We report that a CNN-TDNN neural network model using character decomposition has significantly fewer parameters than the baseline while also improving character error rate.