.png){kind=icon}
返回
Bayesian networks for incomplete data analysis in form processing
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-014-0234-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we study Bayesian network (BN) for form identification based on partially filled fields. It uses electronic ink-tracing files without having any information about form structure. Given a form format, the ink-tracing files are used to build the BN by providing the possible relationships between corresponding fields using conditional probabilities, that goes from individual fields up to the complete model construction. To simplify the BN, we sub-divide a single form into three different areas: header, body and footer, and integrate them together, where we study three fundamental BN learning algorithms: Naive, Peter & Clark and maximum weighted spanning tree. Under this framework, we validate it with a real-world industrial problem i.e., electronic note-taking in form processing. The approach provides satisfactory results, attesting the interest of BN for exploiting the incomplete form analysis problems, in particular.
Keyword:
Bayesian networks
Electronic note-taking
Form processing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
.png){kind=icon}
Sapho Syndrome Associated with Acne Fulminans and Prominent Acromioclavicular Joint InvolvementSAPHO综合征与暴发性痤疮及明显的肩锁关节受累相关
PREAI
分享收藏
Novel and recurrent COL2A1 mutations in Chinese patients with spondyloepiphyseal dysplasia中国脊柱骨骺发育不全患者的COL2A1基因新型及复发性突变
OAAI
OAAI分享收藏分享收藏
查看更多内容