Attention Combination of Sequence Models for Handwritten Chinese Text Recognition
Zhengyu Zhu, Fei Yin, Da‐Han Wang · 2020
Handwritten Chinese text recognition (HCTR) methods can generally be divided into two categories: explicit segmentation based methods and implicit segmentation based methods. The explicit segmentation based approaches are superior in the accuracy rate of character recognition, while the implicit segmentation based approaches can provide more fluent recognition result due to the end-to-end recognition mode of it. In this paper, we propose to combine this two kinds of approaches using convolutional combination strategy. The proposed system includes multiple encoders and a decoder in the form of full convolution. It combines the recognition results of explicit segmentation based HCTR methods and implicit segmentation based HCTR methods, and generates the final recognition output. A novel attention combination mechanism is proposed to solve this one-to-many attention problem. Experiments on the ICDAR13 dataset show that the proposed method improves the accurate rate by 4.96%. The experimental results demonstrate the effectiveness of proposed method.