Automatic signature segmentation using hyper-spectral imaging

Umair Muneer Butt, Sheraz Ahmad, Faisal Shafait, Christian Nansen, Ajmal Saeed Mian, Muhammad Imran Malik

Research output: Chapter in Book/Conference paperConference paper

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2016 15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
EditorsCheng-Lin Liu, Youbin Chen
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages19-24
Number of pages6
ISBN (Electronic)9781509009817
DOIs
Publication statusPublished - 2017
Event15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016 - Shenzhen, China
Duration: 23 Oct 201626 Oct 2016

Conference

Conference15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
CountryChina
CityShenzhen
Period23/10/1626/10/16

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  • Cite this

    Butt, U. M., Ahmad, S., Shafait, F., Nansen, C., Mian, A. S., & Malik, M. I. (2017). Automatic signature segmentation using hyper-spectral imaging. In C-L. Liu, & Y. Chen (Eds.), Proceedings - 2016 15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016 (pp. 19-24). [7814033] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICFHR.2016.0017