| Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters | |
Tao, Dapeng1; Lin, Xu2; Jin, Lianwen2; Li, Xuelong3 ; Tao, DP
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-03-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| ISSN | 2168-2267 |
| 卷号 | 46期号:3页码:756-765 |
| 产权排序 | 3 |
| 摘要 | Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks. For robust CCFR, we integrate a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and develop principal component 2DLSTM (PC-2DLSTM) algorithm. PC-2DLSTM considers two aspects: 1) the principal component layer convolution operation helps remove the noise and get a rational and complete font information and 2) simultaneously, 2DLSTM deals with the long-range contextual processing along scan directions that can contribute to capture the contrast between character trajectory and background. Experiments using the frequently used CCFR dataset suggest the effectiveness of PC-2DLSTM compared with other state-of-the-art font recognition methods. |
| 文章类型 | Article |
| 关键词 | Font Recognition Long Short-term Memory Neurodynamic Models Optical Character Recognition Recurrent Neural Networks (Rnns) |
| 学科领域 | Computer Science, Artificial Intelligence |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2015.2414920 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | RECURRENT NEURAL-NETWORKS ; FEATURE-EXTRACTION ; FEATURE-SELECTION ; CLASSIFICATION ; TIME ; FEATURES ; REPRESENTATION ; ALGORITHMS ; ONLINE ; IMAGES |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| 项目资助者 | National Natural Science Foundation of China(61125106 ; National Science and Technology Support Plan(2013BAH65F01 ; Guangdong Natural Science Funds(2014A030310252) ; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme ; Guangdong Scientific and Technology Research Plan(2012A010701001 ; Shenzhen Technology Project(JCYJ20140901003939001) ; Research Fund for the Doctoral Program of Higher Education of China(20120172110023) ; Shaanxi Key Innovation Team of Science and Technology(2012KCT-04) ; State Key Laboratory of Digital Publishing Technology ; 61075021 ; 2013BAH65F04) ; 2012B091100396) ; 61472144) |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000370963500015 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/27856 |
| 专题 | 光谱成像技术研究室 |
| 通讯作者 | Tao, DP |
| 作者单位 | 1.Yunnan Univ, Sch Informat Sci & Engn, Kunming 650091, Peoples R China 2.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Dapeng,Lin, Xu,Jin, Lianwen,et al. Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(3):756-765. |
| APA | Tao, Dapeng,Lin, Xu,Jin, Lianwen,Li, Xuelong,&Tao, DP.(2016).Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.IEEE TRANSACTIONS ON CYBERNETICS,46(3),756-765. |
| MLA | Tao, Dapeng,et al."Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters".IEEE TRANSACTIONS ON CYBERNETICS 46.3(2016):756-765. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Principal Component （4029KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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