CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Joint Training of Conditional Random Fields and Neural Networks for Stroke Classification in Online Handwritten Documents
Ye, Jun-Yu; Zhang, Yan-Ming; Liu, Cheng-Lin
2016
Conference NameInternational Conference on Pattern Recognition
Source Publication23rd International Conference on Pattern Recognition
Conference DateDec. 04-08, 2016
Conference PlaceCancun, Mexico
Abstract
The task of text/non-text stroke classification in online handwritten documents is an essential preprocessing step in document analysis. It is also a challenging problem since in many cases local features are not enough to generate high accuracy results and contextual information, such as temporal information and spatial information, must be carefully considered. In this paper, we propose a novel method, which jointly trains a combined model of conditional random fields and neural networks, to solve this problem. Both our unary and pairwise potentials are formulated as neural networks. The parameters of conditional random fields and neural networks are learned together during the training process. With much fewer parameters and faster speed, our method achieves impressive performance on the IAMonDo database, a publicly available database of freely handwritten documents.
KeywordCrf
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12263
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorZhang, Yan-Ming
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ye, Jun-Yu,Zhang, Yan-Ming,Liu, Cheng-Lin. Joint Training of Conditional Random Fields and Neural Networks for Stroke Classification in Online Handwritten Documents[C],2016.
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