CRFs and HCRFs Based Recognition for Off-Line Arabic Handwriting

  • Moftah ElzobiEmail author
  • Ayoub Al-Hamadi
  • Laslo Dings
  • Sherif El-etriby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


This paper investigates the application of the probabilistic discriminative based Conditional Random Fields (CRFs) and its extension the hidden-states CRFs (HCRFs) to the problem of off-line Arabic handwriting recognition. A CRFs- and A HCRFs- based classifiers are built on top of an explicit word segmentation module using two different set of shape description features. A simple yet effective taxonomization technique is used to reduce the number of the class labels, and 3000 letter samples from IESK-arDB database are used for the training and 300 words are used for the evaluation. Experiments compare the performance of the CRFs to the HCRFs as well as to that of a generative based HMMs. Results indicate superiority of discriminative based approaches, where HCRFs achieved the best performance followed by CRFs.


Recognition Rate Class Label Hide State Conditional Random Field Gradient Ascent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the National Plan for Science, Technology and Innovation (MAARIFAH) - King Abdulaziz City for Science and Technology(KACST) - KSA. Project code: 13-INF604-10.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Moftah Elzobi
    • 1
    Email author
  • Ayoub Al-Hamadi
    • 1
  • Laslo Dings
    • 1
  • Sherif El-etriby
    • 2
  1. 1.Institute for Information Technology and Communications (IIKT)Otto-von-Guericke-University MagdeburgMagdeburgGermany
  2. 2.Umm Al-Qura UniversityMakkahSaudi Arabia

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