Skip to main content
SpringerLink
Log in

An Assessment of Arabic Handwriting Recognition Technology

  • Chapter
Guide to OCR for Arabic Scripts

Abstract

Automated methods for the recognition of Arabic script are at an early stage compared to their counterparts for the recognition of Latin and Chinese scripts. An assessment of the technology for Arabic handwriting recognition is provided based on the published literature. An introduction to the Arabic script is given followed by a description of algorithms for the processes involved: segmentation, feature extraction, classification, and search. Existing corpora for Arabic are described together with a design for corpus collection. The paper is concluded by identifying technology gaps and providing a bibliography of the recent literature on Arabic recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abuhaiba, I.S.I., Mahmoud, S.A., Green, R.J.: Recognition of handwritten cursive Arabic characters. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 664–672 (1994)

    Article  Google Scholar 

  2. Abuhaiba, I., Holt, M., Datta, S.: Recognition of off-line cursive handwriting. Comput. Vis. Image Underst. 77, 19–38 (1998)

    Article  Google Scholar 

  3. Al-Ohali, Y., Cheriet, M., Suen, C.Y.: Databases for recognition of handwritten Arabic cheques. In: Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition (2000)

    Google Scholar 

  4. Al-Shaher, A., Hancock, E.: Learning mixtures of point distribution models with the EM algorithm. Pattern Recognit. 36, 2805–2818 (2003)

    Article  MATH  Google Scholar 

  5. Al-Yousefi, H., Udpa, S.: Recognition of Arabic characters. IEEE Trans. Pattern Anal. Mach. Intell. 14, 853–857 (1992)

    Article  Google Scholar 

  6. Almaadeed, S., Higgens, C., Elliman, D.: Off-line recognition of handwritten Arabic words using multiple hidden Markov models. Knowl.-Based Syst. 17, 75–79 (2004)

    Article  Google Scholar 

  7. Almuallim, H., Yamaguchi, S.: A method of recognition of Arabic cursive handwriting. IEEE Trans. Pattern Anal. Mach. Intell. 9, 715–722 (1987)

    Article  Google Scholar 

  8. Amara, N.E.B., Bouslama, F.: Classification of Arabic script using multiple sources of information: State of the art and perspectives. Int. J. Doc. Anal. Recognit. 5(4), 195–212 (2005)

    Google Scholar 

  9. Amin, A.: Recognition of hand-printed characters based on structural description and inductive logic programming. Pattern Recognit. Lett. 24, 3187–3196 (2003)

    Article  Google Scholar 

  10. Amin, A., Al-Sadoun, H., Fischer, S.: Hand-printed Arabic character recognition system using an artificial network. Pattern Recognit. 29, 663–675 (1996)

    Article  Google Scholar 

  11. Arivazhagan, M., Srinivasan, H., Srihari, S.: A statistical approach to line segmentation in handwritten documents. In: Proceedings of SPIE (2007)

    Google Scholar 

  12. Ball, G., Srihari, S., Srinivasan, H.: Segmentation-based and segmentation-free methods for spotting handwritten Arabic words. In: IWFHR (2006)

    Google Scholar 

  13. Clocksin, W., Fernando, P.: Towards automatic transcription of Syriac handwriting. In: Proc. Intl Conf. Image Analysis and Processing (2003)

    Google Scholar 

  14. Dehghan, M., Faez, K., Ahmadi, M., Shridhar, M.: Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM. Pattern Recognit. 34, 1057–1065 (2001)

    Article  MATH  Google Scholar 

  15. Dehghani, A., Shabani, F., Nava, P.: Off-line recognition of isolated Persian handwritten characters using multiple hidden Markov models. In: Proc. Intl. Conf. Information Technology: Coding and Computing (2001)

    Google Scholar 

  16. El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Arabic handwriting recognition using baseline dependent features and hidden Markov modeling. In: Proc. Intl. Conf. Document Analysis and Recognition (2005)

    Google Scholar 

  17. El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Arabic handwriting recognition using baseline dependent features and hidden Markov modeling. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  18. Fahmy, M., Ali, S.A.: Automatic recognition of handwritten Arabic characters using their geometrical features. Studies in Informatics and Control J. 10 (2001)

    Google Scholar 

  19. Farah, N., Souici, L., Farah, L., Sellami, M.: Arabic words recognition with classifiers combination: an application to literal amounts. In: Proc. Artificial Intelligence: Methodology, Systems, and Applications (2004)

    Google Scholar 

  20. Farooq, F., Govindaraju, V., Perrone, M.: Pre-processing methods for handwritten Arabic documents. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 1. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  21. Femiani, J.C., Phielipp, M., Razdan, A.: A system for discriminating handwriting from machine print on noisy Arabic datasets. In: SDIUT ’05: Proceedings of the Symposium on Document Image Understanding Technology, College Park, Maryland (2005)

    Google Scholar 

  22. Freeman, H.: Techniques for the digital computer analysis of chain-encoded arbitrary plane curves. In: Proceedings of the National Electronics Conference, vol. 17 (1961)

    Google Scholar 

  23. Goraine, H., Usher, M., Al-Emami, S.: Off-line Arabic character recognition. Computer 25, 71–74 (1992)

    Article  Google Scholar 

  24. Haraty, R., Ghaddar, C.: Arabic text recognition. Int. Arab J. Inf. Technol. 1, 156–163 (2004)

    Google Scholar 

  25. Haraty, R., Hamid, A.: A neuro-heuristic approach for segmenting handwritten Arabic text. In: ACS/IEEE International Conference on Computer Systems and Applications (2001)

    Google Scholar 

  26. Khorsheed, M.: Recognising handwritten Arabic manuscripts using a single hidden Markov model. Pattern Recognit. Lett. 24, 2235–2242 (2003)

    Article  MATH  Google Scholar 

  27. Kim, G., Govindaraju, V.: A lexicon driven approach to handwritten word recognition for real time applications. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 366–379 (1997)

    Article  Google Scholar 

  28. Lorigo, L., Govindaraju, V.: Segmentation and pre-recognition of Arabic handwriting. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 2. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  29. Lorigo, L., Govindaraju, V.: Off-line Arabic handwriting recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 712–724 (2006)

    Article  Google Scholar 

  30. Lorigo, L.M., Govindaraju, V.: Transcript mapping for handwritten Arabic documents. In: Proceedings SPIE (2007, to appear)

    Google Scholar 

  31. Maddouri, S.S., Amiri, H., Belaid, A., Choisy, C.: Combination of local and global vision modeling for Arabic handwritten words recognition. In: Proc. Intl Conf. Frontiers in Handwriting Recognition (2002)

    Google Scholar 

  32. Märgner, V., Pechwitz, M., Abed, H.: ICDAR 2005 Arabic handwriting recognition competition. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 1. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  33. Märgner, V., Pechwitz, M., Abed, H.: ICDAR 2007—Arabic handwriting recognition competition. In: ICDAR ’07: Proceedings of the Tenth International Conference on Document Analysis and Recognition. IEEE Comput. Soc., Los Alamitos (2007)

    Google Scholar 

  34. Miled, H., Amara, N.B.: Planar Markov modeling for Arabic writing recognition: advancement state. In: Proc. Intl. Conf. Document Analysis and Recognition (2001)

    Google Scholar 

  35. Mokbel, C., Akl, H.A., Greige, H.: Automatic speech recognition of Arabic digits over telephone network. In: Proceedings of RTST (2002)

    Google Scholar 

  36. Mozaffari, S., Faez, K., Ziaratban, M.: Character representation and recognition using quad tree-based fractal encoding scheme. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 2. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  37. Mozaffari, S., Faez, K., Ziaratban, M.: Structural decomposition and statistical description of Farsi/Arabic handwritten numeric characters. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 1. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  38. Nadir, F., Abdelatif, E., Tarek, K., Mokhtar, S.: Benefit of multiclassifier systems for Arabic handwritten words recognition. In: ICDAR ’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 1. IEEE Comput. Soc., Seoul (2005)

    Google Scholar 

  39. Olivier, C., Miled, H., Romeo, K., Lecourtier, Y.: Segmentation and coding of Arabic handwritten words. In: Proceedings of the International Conference on Pattern Recognition (1996)

    Google Scholar 

  40. Pechwitz, M., Märgner, V.: HMM based approach for handwritten Arabic word recognition using the IFN/ENIT—database. In: ICDAR ’03: Proceedings of the Seventh International Conference on Document Analysis and Recognition. IEEE Comput. Soc., Edinburgh (2003)

    Google Scholar 

  41. Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H., et al.: IFN/ENIT-database of handwritten Arabic words. In: Proc. CIFED 2002, Hammamet, Tunisia, October 21–23, 2002

    Google Scholar 

  42. Safabakhsh, R., Adibi, P.: Nastaaligh handwritten word recognition using a continuous-density variable-duration HMM. Arab. J. Sci. Eng. 30, 95–118 (2005)

    Google Scholar 

  43. Sari, T., Souici, L., Sellami, M.: Off-line handwritten Arabic character segmentation algorithm: ACSA. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (2002)

    Google Scholar 

  44. Souici, L., Farah, N., Sari, T., Sellami, M.: Rule based neural networks construction for handwritten Arabic city-names recognition. In: Proc. Artificial Intelligence: Methodology, Systems, and Applications (2004)

    Google Scholar 

  45. Souici-Meslati, L., Sellami, M.: A hybrid approach for Arabic literal amounts recognition. Arab. J. Sci. Eng. 29, 174–194 (2004)

    Google Scholar 

  46. Sridharan, K., Farooq, F., Govindaraju, V.: Classification of machine print and handwriting in mixed Arabic documents. In: SDIUT ’05: Proceedings of the Symposium on Document Image Understanding Technology College Park, Maryland (2005)

    Google Scholar 

  47. Srihari, S.N., Tomai, C.I., Zhang, B., Lee, S.: Individuality of numerals. In: ICDAR ’03: Proceedings of the Seventh International Conference on Document Analysis and Recognition. IEEE Comp. Soc., Washington (2003)

    Google Scholar 

  48. Srihari, S., Srinivasan, H., Babu, P., Bhole, C.: Handwritten Arabic word spotting using the CEDARABIC document analysis system. In: SDIUT ’05: Proceedings of the Symposium on Document Image Understanding Technology College Park, Maryland (2005)

    Google Scholar 

  49. Srihari, S.N., Srinivasan, H., Babu, P., Bhole, C.: Handwritten Arabic word spotting using the CEDARABIC document analysis system. In: Proc. Symposium on Document Image Understanding Technology (SDIUT-05), College Park, MD (2005)

    Google Scholar 

  50. Srihari, S., Ball, G., Srinivasan, H.: Versatile search of scanned Arabic handwriting. In: SACH’06: Summit on Arabic and Chinese Handwriting (2006)

    Google Scholar 

  51. Srihari, S., Srinivasan, H., Babu, P., Bhole, C.: Spotting words in handwritten Arabic documents. In: Proceedings SPIE, San Jose, CA (2006)

    Google Scholar 

  52. Touj, S., Amara, N.B., Amiri, H.: Arabic handwritten words recognition based on a planar hidden Markov model. Int. Arab J. Inf. Technol. 2(4), 318–325 (2005)

    Google Scholar 

  53. Zhang, B., Srihari, S.N.: Binary vector dissimilarity measures for handwriting identification. In: Document Recognition and Retrieval X, vol. 5010. SPIE, Bellingham (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, State University of New York, Amherst, NY, 14228, USA

    Sargur N. Srihari & Gregory Ball

Authors
  1. Sargur N. Srihari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gregory Ball
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sargur N. Srihari .

Editor information

Editors and Affiliations

  1. Institute for Communications Technology, Braunschweig Technical University, Schleinitzstraße 22, Braunschweig, 38106, Niedersachsen, Germany

    Volker Märgner

  2. Institute for Communications Technology, Braunschweig Technical University, Schleinitzstraße 22, Braunschweig, 38106, Niedersachsen, Germany

    Haikal El Abed

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London

About this chapter

Cite this chapter

Srihari, S.N., Ball, G. (2012). An Assessment of Arabic Handwriting Recognition Technology. In: Märgner, V., El Abed, H. (eds) Guide to OCR for Arabic Scripts. Springer, London. https://doi.org/10.1007/978-1-4471-4072-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4072-6_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4071-9

  • Online ISBN: 978-1-4471-4072-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation