Pen Tip Motion Prediction for Handwriting Drawing Order Recovery using Deep Neural Network | |
Zhao, Bocheng1,2 | |
2018-08 | |
会议日期 | 2018-08 |
会议地点 | 北京 |
英文摘要 | Pen Tip Motion Prediction (PTMP) is the key step for Chinese handwriting order recovery (DOR), which is a challenge topic in the past few decades. We proposed a novel algorithm framework using Convolutional Neural Network (CNN) to predict pen tip movement for human handwriting pictures. The network is a regression CNN model, whose inputs are a series of part-drawn handwriting images and output is a vector that represents the probability of next stroke point position. The predicted output vector is utilized by an iteration framework to generate pen movement sequences. Experiments on public Chinese and English online handwriting database have indicated that the proposed model performs competitively in multi-writer handwriting PTMP and DOR tasks. Furthermore, the experiment demonstrated that characters belong to different languages shares some common writing patterns and the proposed method could learn these laws effectively |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/38571] |
专题 | 模式识别国家重点实验室_智能交互 |
作者单位 | 1.Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences Beijing, China zhaobocheng2015@ia.ac.cn, mhyang@nlpr.ia.ac.cn, jianhua.tao@ia.ac.cn |
推荐引用方式 GB/T 7714 | Zhao, Bocheng. Pen Tip Motion Prediction for Handwriting Drawing Order Recovery using Deep Neural Network[C]. 见:. 北京. 2018-08. |
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