Journal of Pattern Recognition and Intelligent Systems 
Journal of Pattern Recognition and Intelligent Systems(PRIS)
ISSN:2309-0669(Print)       ISSN:2309-0650(Online)
An Enhanced Bag-of-Features Framework for Arabic Handwritten Sub-words and Digits Recognition
Full Paper(PDF, 973KB)
In recent years, feature learning approaches have gained substantial interest and are successfully applied to challenging problems in facial recognition, visual object retrieval and classification, document image analysis and, recently, in handwriting recognition. In this paper, we present a feature learning framework for Arabic handwritten text recognition based on the Bag-of-Feature (BoF) paradigm. Utilizing the characteristics of handwritten text, we developed two novel versions of SIFT that are discriminative and computationally efficient with half the size of the original SIFT descriptors. To evaluate the quality of the features learned by the framework and the efficiency of the proposed versions of SIFT, we conducted extensive experimental work on two Arabic handwritten text datasets, viz. the non-touching Arabic Indian digit and Arabic sub-words datasets of CENPARMI Bank check database. Our framework achieves state-of-the-art accuracies on both datasets. The recognition performance and the computational efficiency are the result of utilizing the unique properties of the handwritten text.
Keywords:Feature Learning; Bag-of-Features; Arabic Handwriting Recognition; SIFT
Author: Mohammed O. Assayony1, Sabri A. Mahmoud1
1.Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
  1. M. T. Parvez and S. A. Mahmoud, “Offline arabic handwritten text recognition: A Survey,” ACM Comput. Surv., vol. 45, no. 2, pp. 1-35, 2013.
  2. Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, 2013.
  3. A. AbdulKader, “A Two-Tier Arabic Offline Handwriting Recognition Based on Conditional Joining Rules,” in Arabic and Chinese Handwriting Recognition, Springer Berlin Heidelberg, 2008, pp. 70-81.
  4. Y. LeCun, L’. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
  5. R. Anil, K. Manjusha, S. S. Kumar, and K. Soman, “Convolutional Neural Networks for the Recognition of Malayalam Characters,” in Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), vol. 247, pp. 493-500, 2014.
  6. A. Graves and J. Schmidhuber, “Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks,” in Advances in Neural Information Processing Systems (NIPS), vol. 21, pp. 545-552, 2009.
  7. T. Bluche, J. Louradour, M. Knibbe, B. Moysset, M. F. Benzeghiba, and C. Kermorvant, “The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation,” 11th IAPR Int. Work. Doc. Anal. Syst., pp. 161-165, 2014.
  8. U. Porwal, Yingbo Zhou, and V. Govindaraju, “Handwritten Arabic text recognition using Deep Belief Networks,” in 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 302-305.
  9. P. P. Roy, Y. Chherawala, and M. Cheriet, “Deep-Belief-Network Based Rescoring Approach for Handwritten Word Recognition,” in 2014 14th International Conference on Frontiers in Handwriting Recognition, 2014, pp. 506-511.
  10. S. O’Hara and B. A. Draper, “Introduction to the Bag of Features Paradigm for Image Classification and Retrieval,” arXiv Prepr. arXiv1101.3354, 2011.
  11. G. Csurka, R. Dance, Christopher, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Workshop on statistical learning in computer vision (ECCV), 2004, pp. 1-22.
  12. Sivic and Zisserman, “Video Google: a text retrieval approach to object matching in videos,” in Proceedings Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1470-1477, 2003.
  13. L. Rothacker, S. Vajda, and G. a. Fink, “Bag-of-Features Representations for Offline Handwriting Recognition Applied to Arabic Script,” in 2012 International Conference on Frontiers in Handwriting Recognition, 2012, pp. 149-154.
  14. M. Rusinol, D. Aldavert, R. Toledo, and J. Llados, “Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method,” in 2011 International Conference on Document Analysis and Recognition, 2011, pp. 63-67.
  15. S. Fiel and R. Sablatnig, “Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies,” in 2013 12th International Conference on Document Analysis and Recognition (ICDAR), 2013, pp. 545-549.
  16. Y. Al-Ohali, M. Cheriet, and C. Suen, “Databases for recognition of handwritten Arabic cheques,” Pattern Recognit., vol. 36, pp. 111-121, 2004.
  17. T. Tuytelaars and K. Mikolajczyk, “Local Invariant Feature Detectors: A Survey,” Found. Trends® Comput. Graph. Vis., vol. 3, no. 3, pp. 177-280, 2007.
  18. E. Nowak, F. Jurie, and B. Triggs, “Sampling Strategies for Bag-of-Features Image Classification,” in ECCV, vol. 3954, 2006, pp. 490-503.
  19. K. Mikolajczyk and C. Schmid, “Performance evaluation of local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615-30, 2005.
  20. J. Hu, X. Peng, and C. Fu, “A comparison of feature description algorithms,” Opt. - Int. J. Light Electron Opt., vol. 126, no. 2, pp. 274-278, 2015.
  21. D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Nov. 2004.
  22. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Comput. Vis. Image Underst., vol. 110, no. 3, pp. 346-359, 2008.
  23. M. Calonder, V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha, and P. Fua, “BRIEF: Computing a Local Binary Descriptor Very Fast,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1281-1298, 2012.
  24. X. Yang and K. T. T. Cheng, “Local difference binary for ultrafast and distinctive feature description,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 1, pp. 188-194, 2014.
  25. S. A. Mahmoud and W. G. Al-Khatib, “Recognition of Arabic (Indian) bank check digits using log-gabor filters,” Appl. Intell., vol. 35, no. 3, pp. 445-456, 2010.
  26. S. A. Mahmoud, “Recognition of Arabic (Indian) check digits using Spatial Gabor filters,” in 5th IEEE-GCC Conference & Exhibition, 2009, pp. 1-5.
  27. A. Vedaldi and B. Fulkerson, “VLFeat: An open and portable library of computer vision algorithms,” in 18th ACM international conference on Multimedia, 2010, pp. 1469-1472.
  28. S. Awaida and S. A. Mahmoud, “Automatic Check Digits Recognition for Arabic Using Multi-Scale Features, HMM and SVM Classifiers,” Br. J. Math. Comput. Sci., vol. 4, no. 17, pp. 2521-2535, 2014.
  29. V. Romero, A. Giménez, and A. Juan, “Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images,” in IBPRIA ’07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, 2007, vol. 4477, pp. 539-546.
  30. A. Giménez, J. Andrés-Ferrer, A. Juan, and N. Serrano, “Discriminative Bernoulli Mixture Models for Handwritten Digit Recognition,” in 2011 International Conference on Document Analysis and Recognition, 2011, pp. 558-562.
  31. A. G. Pastor, “Bernoulli HMMs for Handwritten Text Recognition,” Ph.D. Thesis, Polytechnic University of Valencia, Spain, 2014.