Weakly Supervised Learning for Over-Segmentation Based Handwritten Chinese Text Recognition
Zhenxing Wang, Qiufeng Wang, Fei Yin, Cheng‐Lin Liu · 2020
In this paper, we proposed a weakly supervised learning method for string-level training of character classifier in over-segmentation based handwritten Chinese text recognition (HCTR). The over-segmentation based framework can easily integrate multiple context models and provide accurate character boundary and recognition confidence, but has not been implemented with string-level training for HCTR. We propose to optimize the character classifier by minimizing the marginal log-likelihood on a string-level annotated handwriting dataset, where the forward-backward algorithm is utilized in a segmentation-and-recognition lattice. Experimental results on the CASIA-HWDB and ICDAR-2013 competition datasets show that the proposed method improves the recognition performance significantly, which demonstrates its effectiveness.