On Optimization of Traditional Chinese Character Recognition

Text recognition in textures is a pressing need for effective contextual perception in our daily life. In OCR scope, deep learning methods can meet the needs of real-time scenarios. However, characters with different textures, such as printed and handwritten characters leads OCR to deal with different scenarios. To this, in this paper, we focus on the implementation and optimization of text recognition using deep learning methods. In particular, we go with a two-stage OCR approach based on deep learning methods to detect and recognize Traditional Chinese characters including handwritten and printed while improving the accuracy. In particular, DBNet [1] is used in the text detection stage and CRNN [2] (baseline) and ABINet [3] (advanced) models in the text recognition stage. For the ABINet, we modify fusion module by using attention mechanism. For the CRNN, we utillize combined loss function. The experiment results show that The CRNN model with the combined loss function improves nearly 4.2% compared with the baseline CRNN. The ABINet model with new fusion module achieves 92.30% on the Traditional Chinese recognition dataset, improves nearly 0.8% compared to the original ABINet. Notably, we also have our own Traditional Chinese handwritten datasets for text detection and recognition. In fine, we believe that our endeavour can help grow the community in a better way.