您好，欢迎访问浙江省农业科学院 机构知识库！
浙江省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Automatic Signature Segmentation Using Hyper-spectral Imaging

文献类型： 外文期刊

作者： Butt, Umair Muneer 1 ; Ahmad, Sheraz 2 ; Shafait, Faisal 1 ; Nansen, Christian 3 ; Mian, Ajmal Saeed 5 ; Malik, Muh 1 ;

作者机构： 1.Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, Islamabad, Pakistan

2.German Res Ctr Artificial Intelligence DFKI, Kaiserslautern, Germany

3.Univ Calif Davis, Davis, CA 95616 USA

4.Zhejiang Acad Agr Sci, Hangzhou, Zhejiang, Peoples R China

5.Univ Western Australia, Perth, WA, Australia

关键词： Local Feature Analysis;SURF;Signature Segmentation;Hyper-spectral Imaging

期刊名称：PROCEEDINGS OF 2016 15TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR)

ISSN： 2167-6445

年卷期： 2016 年

页码：

收录情况： SCI

摘要： In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity of the ink, and by any structural information of the text, as the classification relies exclusively on the spectral response of the document. The proposed method can extract signature pixels either overlapping or non-overlapping from different backgrounds like, logos, tables, stamps, and printed text. We used high-resolution hyper-spectral imaging to study and classify 300 documents with varying backgrounds. We evaluated the proposed classification method and compared results with the state-of-the art system. The proposed method outperformed the state-of-the-art system and achieved 100% precision and 84% recall.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：浙江省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台