Faster Segmentation-Free Handwritten Chinese Text Recognition with Character Decompositions
Théodore Bluche, Ronaldo Messina · 2016
Recently, segmentation-free methods for handwritten Chinese text were proposed. They do not require character-level annotations to be trained, and avoid character segmentation errors at decoding time. However, segmentation-free methods need to make at least as many predictions as there are characters in the image, and often a lot more. Combined with the fact that there are many characters in Chinese, these systems are too slow to be suited for industrial applications. Inspired by the input methods for typing Chinese characters, we propose a sub-character-level recognition that achieves a 4x speedup over the baseline Multi-Dimensional Long Short-Term Memory Recurrent Neural Network (MDLSTM-RNN).