A Semantic Segmentation-based Method for Handwritten Japanese Text Recognition
Kha Cong Nguyen, Cuong Tuan Nguyen, Masaki Nakagawa · 2020
Recently, the segmentation-free approach using Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for handwritten text recognition has been investigated by many research groups. Although good results are produced on some datasets, there remain some drawbacks. It is neither robust against the change of handwriting styles such as gaps between characters, stroke widths and shapes of characters nor stable for skewed, and curved text lines unless heavily trained by such patterns. In this paper, we propose a segmentation-based method for handwritten Japanese text recognition. The method employs a semantic segmentation model for precisely splitting text lines into single characters. The semantic segmentation model is based on an encoder-decoder architecture like U-Net, but we employ available techniques to improve the accuracy of pixel classification. They are a deeper encoder with ResNet 101, dilated convolutions and Spatial Pyramid Pooling. Subsequently, a CNN based OCR is used to recognize segmented character images. Finally, the recognized candidates are considered in a lattice diagram combined with a linguistic context. The result of experiments shows that the segmentation model increases the mean IoU significantly from 89.32% to 94.96% and the proposed approach is robust to the change of handwriting styles while a segmentation-free method is quite sensitive to them, resulting in the significant reduction of the recognition rate.