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Local Orientation Extraction for Wordspotting in Syriac Manuscripts

  • P. Bilane
  • S. Bres
  • H. Emptoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

This paper presents a contribution to Word Spotting applied for digitized Syriac manuscripts. The Syriac language was wrongfully accused of being a dead language and has been set aside by the domain of handwriting recognition. Yet it is a very fascinating handwriting that combines the word structure and calligraphy of the Arabic handwriting with the particularity of being intentionally written tilted by an angle of approximately 45°. For the spotting process, we developed a method that should find all occurrences of a certain query word image, based on a selective sliding window technique, from which we extract directional features and afterwards perform a matching using Euclidean distance correspondence between features. The proposed method does not require any prior information, and does not depend of a word to character segmentation algorithm which would be extremely complex to realize due to the tilted nature of the handwriting.

Keywords

Word Spotting orientation features directional roses 

References

  1. 1.
    Balasubramanian, A., Meshesha, M., Jawahar, C.V.: Retrieval from document image collections. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Clocksin, W.F., Fernando, P.P.J.: Towards automatic transcription of Syriac handwriting. In: IEEE Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP 2003), Mantova, Italy, September 2003, pp. 664–669 (2003)Google Scholar
  3. 3.
    Clocksin, W.F.: Handwritten Syriac character recognition using order structure invariance. In: IEEE Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge, UK, pp. 562–565 (August 2004)Google Scholar
  4. 4.
    Eglin, V., Bres, S., Rivero, C.: Hermite and Gabor transforms for noise reduction and handwriting classification in ancient manuscripts. International Journal on Document Analysis and Recognition (IJDAR 2007) 9, 101–122 (2007)CrossRefGoogle Scholar
  5. 5.
    Gatos, B., Pratikakis, I., Perantonis, S.J.: An adaptative binarization technique for low quality historical documents. In: Marinai, S., Dengel, A. (eds.) DAS 2004. LNCS, vol. 3163, pp. 102–113. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Gatos, B., Pratikakis, I., Perantonis, S.J.: Adaptive degraded document image binarization., Pattern Recognition. The Journal of the Pattern Recognition Society 39, 317–327 (2006)zbMATHCrossRefGoogle Scholar
  7. 7.
    Leydier, Y., Lebourgeois, F., Emptoz, H.: Text search for medieval manuscript images, Pattern Recognition. The Journal of the Pattern Recognition Society 40, 3552–3567 (2007)zbMATHCrossRefGoogle Scholar
  8. 8.
    Manmatha, R., Rothfeder, J.L.: A scale space approach for automatically segmenting words from historical handwritten documents. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI 2005) 27, 1212–1225 (2005)CrossRefGoogle Scholar
  9. 9.
    Nicolas, S., Paquet, T., Heutte, L.: Extraction de la structure de documents manuscrits complexes à l’aide de champs Markoviens. In: Actes du 9ème Colloque International Francophone sur l’Ecrit et le Document (CIFED 2006), pp. 124-129 (September 2006)Google Scholar
  10. 10.
    Rath, T.M., Manmatha, R.: Word spotting for historical documents. International Journal on Document Analysis and Recognition (IJDAR 2007) 9, 139–152 (2007)CrossRefGoogle Scholar
  11. 11.
    Terasawa, K., Nagasaki, T., Kawashima, T.: Eigenspace method for text retrieval in historical document images. In: IEEE Proceedings of the 8th International Conference on Document Analysis and Recognition (ICDAR 2005), Seoul, Korea, August 2005, pp. 437–441 (2005)Google Scholar
  12. 12.
    Terasawa, K., Tanaka, Y.: Locality sensitive pseudo-code for document images. In: IEEE Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR 2007), Curitiba, Brazil, September 2007, pp. 73–77 (2007)Google Scholar
  13. 13.
    Vinciarelli, A., Bengio, S., Bunke, H.: Offline recognition of unconstrained handwritten texts using hmms and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI 2004) 26, 709–720 (2004)CrossRefGoogle Scholar
  14. 14.
    Weihua, H., Tan, C.L., Sung, S.Y., Xu, Y.: Word shape recognition for image-based document retrieval. In: IEEE Proceedings of the International Conference on Image Processing (ICIP 2001), Thessaloniki, Greece, October 2001, pp. 1114–1117 (2001)Google Scholar
  15. 15.
    Allier, B., Emptoz, H.: Degraded character image restoration using active contours: A first approach. In: Proceedings of the ACM Symposium on Document Engineering, Virginia, USA, pp. 142–148 (2002)Google Scholar
  16. 16.
    Zheng, Q.J., Kanungo, T.: Morphological degradation models and their use in document image restoration. In: IEEE Proceedings of the International Conference on Image Processing (ICIP 2001), Thessaloniki, Greece, October 2001, pp. 193–196 (2001)Google Scholar
  17. 17.
    Allier, B., Bali, N., Emptoz, H.: Automatic accurate broken character restoration for patrimonial documents. International Journal on Document Analysis and Recognition (IJDAR 2006) 8, 246–261 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • P. Bilane
    • 1
  • S. Bres
    • 1
  • H. Emptoz
    • 1
  1. 1.LIRISINSA-LyonFrance

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