Affiliations: [a] School of Information Science and Engineering, Huaqiao University, Xiamen, China
| [b] School of Engineering, Huaqiao University, Quanzhou, China
Correspondence:
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Corresponding author. Huanqiang Zeng, School of Information Science and Engineering, Huaqiao University, Xiamen 361021, China. E-mail: [email protected].
Abstract: Handwritten numeral recognition is a challenging problem in the character recognition field due to the large variation in the writing styles of different persons and high similarity in the contour of different numerals. To address this problem, an effective multi-task learning network (MTLN) for handwritten numeral recognition is presented in this paper. Based on the observation that the writing style could play an effective complementary role to the learned feature extracted from numerals, the proposed MTLN simultaneously performs the handwritten numeral learning module and the writing style learning module. Consequently, the determination of scratchy/non-scratchy in the writing style learning module can effectively assist the handwritten numeral learning module to obtain a more robust and distinguishable feature so as to improve the recognition performance. Extensive experiments on multiple existing handwritten numeral datasets have demonstrated that the proposed MTLN can effectively improve the recognition accuracy, and outperform multiple state-of-the-art methods.
