Radical region based CNN for offline handwritten Chinese character recognition

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In recent years, deep learning based methods have been widely used in handwritten Chinese character recognition (HCCR) and greatly improved the recognition accuracy. However, most of the current methods simply employ famous networks like GoogleNet without fully embedding the specific features of Chinese characters. Taking structural characteristics into consideration, we propose a radical region network structure to represent the radical region information (For example left, right, top and bottom radical regions). In our study, the character feature is represented as global feature while the radical region feature is represented as local feature. The multi-supervised training method is also used to learn two kinds of feature at the same time. Experiment results show the proposed methods improve recognition accuracy of current models. The performance of the best model has been raised to 97.42% on ICDAR 2013 offline HCCR competition database which achieves the state-of-The-Art result as we know.

本文言語English
ホスト出版物のタイトルProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ548-553
ページ数6
ISBN(電子版)9781538633540
DOI
出版ステータスPublished - 2018 12 13
イベント4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
継続期間: 2017 11 262017 11 29

出版物シリーズ

名前Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
国/地域China
CityNanjing
Period17/11/2617/11/29

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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