Work

FINet: Handwriting trajectory reconstruction of Chinese characters based on the font imitate network

Year: 2024

Type: article

Abstract: Recovering online handwriting trajectory from Chinese handwritten images is still a difficult problem because of abundant strokes, complex structures and different writing styles of Chinese characters. To address this problem, this paper proposes a handwriting trajectory reconstruction method for Chinese characters called the Font Imitate Network. The Font Imitate Network integrates two networks, the spatial encoder uses HRNet to encode the spatial association information of each position on the offline image; the temporal decoder consists of a multilayer perceptron and a recurrent neural network.The stroke feature network is designed to help predict the next handwriting point by focusing on the stroke path in the form of heatmap. Experiments are conducted on the CASIA-OLHWDB1.1 with DTW, LDTW, RMSE and character recognition accuracy as the evaluation metrics. The experimental results show our FINet outperforms the state-of-the-art methods and the recognition accuracy of offline characters is improved by the handwriting reconstruction.

Source: Pattern Recognition

DOI: 10.1016/j.patcog.2024.110949

Authors: Yuanping Zhu, Shengnan Li, Hui Wang, Feilong Wei

Institution: Tianjin Normal University

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,710

Funder: Natural Science Foundation of Tianjin City