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Stamp detection in scanned documents

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents current challenges in stamp detection problem. It is a crucial topic these days since more and more traditional paper documents are being scanned in order to be archived, sent through the net or just printed. Moreover, an electronic version of paper document stored on a hard drive can be taken as forensic evidence of possible crime. The main purpose of the method presented in the paper is to detect, localize and segment stamps (imprints) from the scanned document. The problem is not trivial since there is no such thing like stamp standard. There are many variations in size, shape, complexity and ink color. It should be remembered that the scanned document may be degraded in quality and the stamp can be placed on a relatively complicated background. The algorithm consists of several steps: color segmentation and pixel classification, regular shapes detection, candidates segmentation and verification. The paper includes also the initial results of selected experiments on real documents having different types of stamps.
Rocznik
Strony
61--68
Opis fizyczny
Bibliogr. 11 poz., rys.
Twórcy
  • Multimedia Systems, West Pomeranian University of Technology, Żołnierska 49, 71-210 Szczecin, Poland
Bibliografia
  • [1] Zhu, G., Jaeger, S., Doermann, D., A robust stamp detection framework on degraded documents, International Conference on Document Recognition and Retrieval XIII (IS&T, SPIE, San Jose, 2006): 1–9.
  • [2] Ueda, K., Nakamura, Y., Automatic verification of seal impression patterns, Proc. 7th. Int. Conf. on Pattern Recognition (Montreal, 1984): 1019–1021.
  • [3] Zhu, G., David Doermann, D., Automatic Document Logo Detection, The 9th International Conference on Document Analysis and Recognition (ICDAR, Curitiba, 2007): 864–868.
  • [4] Pham, T. D., Unconstrained logo detection in document images, Pattern Recognition 36(12) (2003): 3023–3025.
  • [5] Zhang, D., Lu, G., Review of shape representation and description techniques, Pattern Recognition 37(1) (2004): 1–19.
  • [6] Loncaric, S., A survey on shape analysis techniques, Pattern Recognition 31(8) (1998): 983–1001.
  • [7] Mehtre, B., M., Kankanhalli, M. S., Lee, W., F., Shape measures for content based image retrieval: a comparison, Information Proc. & Management 33 (1997): 319–337.
  • [8] Wood, J., Invariant pattern recognition: a review, Pattern Recognition 29(1) (1996): 1–17.
  • [9] Deng, Y., Manjunath, B. S., Kenney, C., Moore, M. S., Shin, H., An efficient color representation for image retrieval, IEEE Transactions on Image Processing 10(1) (2001): 140–147.
  • [10] Manjunath, B. S., Ohm, J.-R., Vasudevan, V. V., Yamada, A., Color and texture descriptors, IEEE Transactions on Circuits and Systems for Video Technology 11(6) (2001): 703–715.
  • [11] Jain, A. K., Fundamentals of Digital Image Processing (Prentice Hall, Upper Saddle River, 1989).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-abc4bfa1-d5b1-4a15-a061-a99a8504f1f1
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