Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters
文献类型：期刊论文
| 作者 | Tao, Dapeng1; Lin, Xu2; Jin, Lianwen2; Li, Xuelong3
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-03-01 |
| 卷号 | 46期号:3页码:756-765 |
| ISSN号 | 2168-2267 |
| 关键词 | Font recognition long short-term memory neurodynamic models optical character recognition recurrent neural networks (RNNs) |
| 通讯作者 | tao, dp |
| 产权排序 | 3 |
| 英文摘要 | 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. |
| 学科主题 | computer science, artificial intelligence ; computer science, cybernetics |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | recurrent neural-networks ; feature-extraction ; feature-selection ; classification ; time ; features ; representation ; algorithms ; online ; images |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000370963500015 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27856]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 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.(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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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