Title | ||
---|---|---|
Radical Aggregation Network for Few-Shot Offline Handwritten Chinese Character Recognition |
Abstract | ||
---|---|---|
Offline handwritten Chinese character recognition has attracted much interest due to its various applications. The most cutting-edge methods treat Chinese character as a whole, ignoring the structures and radicals that compose characters. To use the radical-level composition of Chinese characters and achieve few-shot/zero-shot Chinese character recognition, some methods attempt to recognize Chinese characters at the radical level; however, these methods have shown poor performance due to weak radical feature representation and the use of inflexible decoding algorithm. In this paper, a novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition. The RAN consists of three components, a radical mapping encoder (RME), a radical aggregation module (RAM), and a character analysis decoder (CAD). Experiments show that our method can effectively recognize unseen handwritten characters given few support samples, while maintaining a high performance on seen characters. (C) 2019 The Authors. Published by Elsevier B.V. |
Year | DOI | Venue |
---|---|---|
2019 | 10.1016/j.patrec.2019.08.005 | PATTERN RECOGNITION LETTERS |
Keywords | DocType | Volume |
Handwritten Chinese character recognition,Chinese radical recognition,Deep learning,Few-shot learning | Journal | 125 |
ISSN | Citations | PageRank |
0167-8655 | 3 | 0.39 |
References | Authors | |
0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Tianwei Wang | 1 | 10 | 4.97 |
zecheng xie | 2 | 96 | 7.55 |
Zhe Li | 3 | 30 | 16.58 |
Lianwen Jin | 4 | 1337 | 113.14 |
Xiangle Chen | 5 | 3 | 0.39 |