International Journal of Computer Vision

, Volume 94, Issue 1, pp 118–135 | Cite as

Identifying Join Candidates in the Cairo Genizah

  • Lior WolfEmail author
  • Rotem Littman
  • Naama Mayer
  • Tanya German
  • Nachum Dershowitz
  • Roni Shweka
  • Yaacov Choueka


A join is a set of manuscript-fragments that are known to originate from the same original work. The Cairo Genizah is a collection containing approximately 350,000 fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in libraries and private collections worldwide, and there is an ongoing effort to document and catalogue all extant fragments. The task of finding joins is currently conducted manually by experts, and presumably only a small fraction of the existing joins have been discovered. In this work, we study the problem of automatically finding candidate joins, so as to streamline the task. The proposed method is based on a combination of local descriptors and learning techniques. To evaluate the performance of various join-finding methods, without relying on the availability of human experts, we construct a benchmark dataset that is modeled on the Labeled Faces in the Wild benchmark for face recognition. Using this benchmark, we evaluate several alternative image representations and learning techniques. Finally, a set of newly-discovered join-candidates have been identified using our method and validated by a human expert.


Cairo Genizah Document analysis Similarity learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Baird, K. (1992). Anatomy of a versatile page reader. Proceedings of the IEEE, 80(7), 1059–1065. CrossRefGoogle Scholar
  2. Barnes, C., Shechtman, E., Finkelstein, A., & Goldman, D. B. (2009). PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics, 28(3) (Proceedings of the SIGGRAPH) Google Scholar
  3. Bensefia, A., Paquet, T., & Heutte, L. (2003). Information retrieval based writer identification. In Proceedings of the seventh international conference on document analysis and recognition (pp. 946–950). CrossRefGoogle Scholar
  4. Bilenko, M., Basu, S., & Mooney, R. J. (2004). Integrating constraints and metric learning in semi-supervised clustering. In ICML ’04: Proceedings of the twenty-first international conference on machine learning (p. 11). New York: ACM. CrossRefGoogle Scholar
  5. Bres, S., Eglin, V., & VolpilhacAuger, C. (2006). Evaluation of handwriting similarities using hermite transform. In G. Lorette (Ed.), Tenth international workshop on frontiers in handwriting recognition. La Baule: Suvisoft. Google Scholar
  6. Bulacu, M., & Schomaker, L. (2007). Automatic handwriting identification on medieval documents. In Fourteenth international conference on image analysis and processing, ICIAP (pp. 279–284). CrossRefGoogle Scholar
  7. Casey, R., & Lecolinet, E. (1996). A survey of methods and strategies in character segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 690–706. CrossRefGoogle Scholar
  8. Cristianini, N., Shawe-Taylor, J., Elissee, A., & Kandola, J. (2002). On kernel-target alignment. Advances in Neural Information Processing Systems, 14, 367–373. Google Scholar
  9. Dance, C., Willamowski, J., Fan, L., Bray, C., & Csurka, G. (2004). Visual categorization with bags of keypoints. In ECCV workshop on statistical learning in computer vision. Google Scholar
  10. Dinstein, I., & Shapira, Y. (1982). Ancient Hebraic handwriting identification with run-length histograms. IEEE Transactions on Systerms Man and Cybernetics, 12, 405–409. CrossRefGoogle Scholar
  11. Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. MathSciNetCrossRefGoogle Scholar
  12. Guillaumin, M., Verbeek, J., & Schmid, C. (2009). Is that you? Metric learning approaches for face identification. In International conference on computer vision, sep. 2009. Google Scholar
  13. Hertz, T., Bar-Hillel, A., & Weinshall, D. (2004). Boosting margin based distance functions for clustering. In International conference on machine learning, ICML, 2004. Google Scholar
  14. Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: a database for studying face recognition in unconstrained environments. TR 07-49, UMASS. Google Scholar
  15. Huang, G., Jones, M., & Learned-Miller, E. (2008). LFW results using a combined Nowak plus MERL recognizer. In ECCV faces in real-life images workshop. Google Scholar
  16. Ke, Y., & Sukthankar, R. (2004). Pca-sift: a more distinctive representation for local image descriptors. In Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, CVPR 2004 (Vol. 2, pp. II–506–II–513) Google Scholar
  17. Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009). Attribute and simile classifiers for face verification. In IEEE international conference on computer vision, ICCV, Oct. 2009. Google Scholar
  18. Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In 2006 IEEE computer society conference on computer vision and pattern recognition (Vol. 2, pp. 2169–2178) Google Scholar
  19. Leedham, G., Varma, S., Patankar, A., & Govindarayu, V. (2002). Separating text and background in degraded document images; a comparison of global threshholding techniques for multi-stage threshholding. In International workshop on frontiers in handwriting recognition (pp. 244–249). CrossRefGoogle Scholar
  20. Lerner, H. G., & Jerchower, S. (2006). The Penn/Cambridge Genizah fragment project: issues in description, access, and reunification. Cataloging & Classification Quarterly, 42(1), 21–39. CrossRefGoogle Scholar
  21. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. CrossRefGoogle Scholar
  22. Panagopoulos, M., Papaodysseus, C., Rousopoulos, P., Dafi, D., & Tracy, S. (2009). Automatic writer identification of ancient Greek inscriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(8), 1404–1414. CrossRefGoogle Scholar
  23. Pinto, N., DiCarlo, J., & Cox, D. (2009). How far can you get with a modern face recognition test set using only simple features? In IEEE computer society conference on computer vision and pattern recognition, pp. 2591–2598 Google Scholar
  24. Reif, S. C. (2000). A Jewish archive from Old Cairo: the history of Cambridge University’s Genizah collection. Richmond: Curzon Press. Google Scholar
  25. Serre, T., Wolf, L., & Poggio, T. (2005). Object recognition with features inspired by visual cortex. In IEEE computer society conference on computer vision and pattern recognition, CVPR (Vol. 2, pp. 994–1000). Google Scholar
  26. Shental, N., Hertz, T., Weinshall, D., & Pavel, M. (2006). Adjustment learning and relevant component analysis. In Computer vision, ECCV (pp. 181–185). Google Scholar
  27. Simakov, D., Caspi, Y., Shechtman, E., & Irani, M. (2008). Summarizing visual data using bidirectional similarity. In IEEE conference on computer vision and pattern recognition, CVPR 2008 (pp. 1–8). Google Scholar
  28. Srihari, S. N., & Govindaraju, V. (1989). Analysis of textual images using the Hough transform. Machine Vision and Applications, 2(3), 141–153. CrossRefGoogle Scholar
  29. Taigman, Y., Wolf, L., & Hassner, T. (2009). Multiple one-shots for utilizing class label information. In The British machine vision conference, BMVC, Sept. 2009. Google Scholar
  30. Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244. Google Scholar
  31. Wolf, L., Bileschi, S., & Meyers, E. (2006). Perception strategies in hierarchical vision systems. In 2006 IEEE computer society conference on computer vision and pattern recognition (Vol. 2, pp. 2153–2160). Google Scholar
  32. Wolf, L., Hassner, T., & Taigman, Y. (2008). Descriptor based methods in the wild. In Faces in real-life images workshop in ECCV. Google Scholar
  33. Wolf, L., Hassner, T., & Taigman, Y. (2009). The one-shot similarity kernel. In IEEE international conference on computer vision, ICCV, Sept. 2009. Google Scholar
  34. Wolf, L., Littman, R., Mayer, N., Dershowitz, N., Shweka, R., & Choueka, Y. (2009). Automatically identifying join candidates in the Cairo Genizah. In Post ICCV workshop on eHeritage and digital art preservation, Sept. 2009. Google Scholar
  35. Wolf, L., Taigman, Y., & Hassner, T. (2009). Similarity scores based on background samples. In Asian computer vision conference, ACCV, Sept. 2009. Google Scholar
  36. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. CrossRefGoogle Scholar
  37. Xing, E., Ng, A. Y., Jordan, M., & Russell, S. (2003). Distance metric learning, with application to clustering with side-information. In NIPS. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Lior Wolf
    • 1
    Email author
  • Rotem Littman
    • 1
  • Naama Mayer
    • 1
  • Tanya German
    • 1
  • Nachum Dershowitz
    • 1
  • Roni Shweka
    • 2
  • Yaacov Choueka
    • 2
  1. 1.The Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.The Friedberg Genizah ProjectJerusalemIsrael

Personalised recommendations