Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition
This addresses the problem of accurate and efficient recognition of handwritten Chinese text for applications like document digitization, though it is incremental as it builds on existing neural network methods.
The paper tackles handwritten Chinese text recognition by proposing an end-to-end fully convolutional recurrent network (FCRN) that directly processes pen-tip trajectories without segmentation, achieving state-of-the-art correct rates of 96.40% and 95.00% on benchmark datasets.
This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract signature features from the input pen-tip trajectory, a fully convolutional network to learn informative representation, a sequence modeling layer to make per-frame predictions on the input sequence and a transcription layer to translate the predictions into a label sequence. The FCRN is end-to-end trainable in contrast to conventional methods whose components are separately trained and tuned. We also present a refined beam search method that efficiently integrates the language model to decode the FCRN and significantly improve the recognition results. We evaluate the performance of the proposed method on the test sets from the databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition competition, and both achieve state-of-the-art performance with correct rates of 96.40% and 95.00%, respectively.