Teaching machines to write like humans using L-attributed grammar
文献类型：期刊论文
| 作者 | Shao, Yunxue3; Liu, Cheng-Lin1,2
|
| 刊名 | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
 |
| 出版日期 | 2020-04-01 |
| 卷号 | 90页码:10 |
| 关键词 | Automatic writing Handwritten character recognition L-attributed grammar Top-down derivation |
| ISSN号 | 0952-1976 |
| DOI | 10.1016/j.engappai.2020.103489 |
| 通讯作者 | Shao, Yunxue(csshyx@njtech.edu.cn) |
| 英文摘要 | Reading and writing are easy for humans. The automatic reading of handwritten characters has been studied for several decades. Machine learning algorithms for reading tasks often require a huge amount of data to perform with similar accuracy to humans, yet it is also difficult to gain sufficient meaningful data. Automatic writing tasks have not been studied as extensively. In this paper, we teach machines to write like teaching a child by telling the machine the method for writing each character using L-attributed grammar. With the aid of the proposed TMTW (Teaching Machines To Write) interacting system, a human as a teacher only needs to provide the writing sequence of parts and control lines. The proposed system automatically perceives the relationships between control lines and parts, and constructs the grammars. Top-down derivation and the stroke generation method are applied to generate varying characters based on the learned grammars. For as long as a machine can write, it can be applied in robot control or training sample generation for automatic reading tasks. The MNIST and CASIA datasets are used to demonstrate the effectiveness of the proposed system on different languages. The machine written samples are used to train a network, which is evaluated on the MNIST test set. A test error rate of 1.23% is achieved using only approximately 20 grammars on average for each digit. Using the generated and handwritten samples together as a training set can reduce the test error rate to 0.61%. Similar experiments are conducted using the CASIA data set, and the results demonstrated that the proposed method is effective in generating characters with a complex structure. |
| WOS关键词 | CHINESE CHARACTERS ; RECOGNITION ; ONLINE |
| 资助项目 | National Natural Science Foundation of China (NSFC)[61563039] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000528194400011 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39361]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Shao, Yunxue |
| 作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shao, Yunxue,Liu, Cheng-Lin. Teaching machines to write like humans using L-attributed grammar[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2020,90:10. |
| APA | Shao, Yunxue,&Liu, Cheng-Lin.(2020).Teaching machines to write like humans using L-attributed grammar.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,90,10. |
| MLA | Shao, Yunxue,et al."Teaching machines to write like humans using L-attributed grammar".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 90(2020):10. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。