The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents

  • Yusuf Parwej Al Baha University Al Baha, Kingdom of Saudi Arabia (KSA)
Keywords: Arabic Script, Dynamic Time Warping (DTW), Word Spotting, Segmentation, Bidirectional Long-Short-Term Memory Neural Network (BLSTMNN), Euclidean Distance


The reflow from Arabic document image collections is a challenging task. This is partly due to the insolubility of the Arabic script. Because of the peculiarity of the whole body of the Arabic words, namely connectivity between the characters, thereby the segmentation of An Arabic word is very arduous and also the variability of the handwritten styles and shapes as well as deceleratation in the print therefore Arabic document repositories are not liable to indexation and reflow. In this paper, we are proposing an idea for reflow coherent Arabic document in response to an appropriate Arabic query word. We present a novel approach at reflow on an Arabic document image, using a Bidirectional Long-Short-Term Memory Neural Network. The designed to take relating to information into account, these networks can maintain Arabic word images that can not be durable segmented into individual Arabic characters. The partitioning Arabic word, we easier the problem and receive elevated reflow rates. The proposed capable reflow scheme avoids unambiguous recognition of Arabic characters. An experimental evaluation on a dataset of Arabic word images conjunct from handwritten notebook show good precision even in the impendence of printing transformation and deceleratation. The reflow Arabic word performance is comparison with baseline methods. These results encourage the development of real world systems for word reflow for Arabic documents.

Author Biography

Yusuf Parwej, Al Baha University Al Baha, Kingdom of Saudi Arabia (KSA)

Assistant Professor,

Department of Computer Science & Engg.,

Al Baha University , Al Baha,

Kingdom of Saudi Arabia (KSA)



S. V. Rice, G. Nagy, and T. A. Nartker, Optical Character Recognition: An Illustrated Guide to the Frontier. Kluwer, 1999.

H. Alamri, J. Sadri, C. Y. Suen, and N. Nobile. A novel

comprehensive database for Arabic off-line handwriting recognition. In Proc. 11th Int. Conf. on Frontiers in Handwriting Recognition (ICFHR), pages 664–669, 2008.

T. M. Rath and R. Manmatha, “Word spotting for historical documents,” IJDAR, vol. 9, no. 2-4, pp. 139–152, 2007.

Pramod Sankar K., C. V. Jawahar and R. Manmatha, “Nearest Neighbor based Collection OCR,” in Proc. DAS, 2010.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–80, 1997.

V. Frinken, A. Fischer, R. Manmatha, and H. Bunke. A

novel word spotting method based on recurrent neural networks. IEEE Trans. on Pattern Analysis and Machine Intelligence, 34(2):211–224, 2012.

F. Eyben, S. Bock, B. Schuller, and A. Graves, “Universal onset detection with bidirectional long short-term memory neural networks,” in Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR 2010), 2010.

M. Cheriet, M. Beldjehem. , “ Visual Processing of Arabic Handwriting:Challenges and New Directions” , Summit on Arabic and Chinese handwriting (SACH’06), Washington-DC, USA, pp 129-136, 2006.

J. Dichy ,” On lemmatization in Arabic. A formal definition of the Arabic entries of multilingual lexical databases “ , ACL 39th Annual Meeting. Workshop on Arabic Language Processing; Status and Prospect. Toulouse, pp 23-30, 2001.

MA. Attia , T. Salakoski , F. Ginter , S. T. Pyysalo ,” Accommodating Multiword Expressions in an Arabic LFG Grammar “, In Finland Springer-Verlag Berlin Heidelberg, vol 4139, pp 87 – 98, 2006

R. Moghaddam and M. Cheriet. Application of multilevel classifier and clustering for automatic word spotting in historical document images. In Proc. 10th Int. Conf. on Document Analysis and Recognition (ICDAR), pages 511–515, 2009.

T. van der Zant, L. Schomaker, and K. Haak. Handwritten-word spotting using biologically inspired features. Pattern Analysis and Machine Intelligence, IEEE

Transactions on, 30(11):1945 –1957, Nov. 2008.

A. Kolz, J. Alspector, M. Augusteijn, R. Carlson, and G. V. Popescu, “A Line-oriented Approach to Word Spotting in Handwritten Documents,” Pattern Analysis and Applications, 2(3), pp. 153–168, 2000.

B. Gatos and I. Pratikakis, “Segmentation-free word spotting in historical printed documents,” in International Conference on Document Analysis and Recognition, july 2009, pp. 271– 275.

M. Burl and P. Perona, “Using Hierarchical Shape Models to Spot Keywords in Cursive handwriting,” IEEECS Conference on Computer Vision and Pattern Recognition, pp. 535–540, June 1998.

Joseph B. Kruskal and Mark Liberman. The symmetric time-warping problem: from continuous to discrete. In David Sanko and Joseph B. Kruskal, editors, Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparisons. Addison-Wesley, Reading, Massachusetts, 1983.

C. S. Myers and L. R. Rabiner, “A Comparative Study of Several Dynamic Time-warping Algorithms for Connected Word Recognition,” The Bell System Technical Journal, 60(7), pp. 1389- 1409, September 1981.

Toni M. Rath and R. Manmatha. Word image matching using dynamic time warping. In Proceedings of the Conference on Computer Vision and Pattern Recognition, volume 2, pages 521- 527, Madison, WI, USA, 2003.

M. Schuster and K. K. Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45:2673–2681, November 1997.

M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Transactions on Signal Processing, vol. 45, pp. 2673–2681, 1997.

Yusuf Perwej , Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network” for published in the International Journal of Scientific & Engineering Research (IJSER), France , Vol.3, Issue 6, June 2012, Pages 1- 9, ISSN 2229 – 5518.

A. Graves, S. Fernandez, and J. Schmidhuber, “Bidirectional lstm networks for improved phoneme classification and recognition,” in Proc. of ICANN, Warsaw, Poland, 2005, pp. 602–610.

S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In S. C. Kremer and J. F. Kolen, editors, A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, 2001.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

A. Graves and J. Schmidhuber. “Framewise phoneme

classification with bidirectional LSTM and other neural network architectures”, Neural Networks, 18(6):602– 610, 2005.

M. Wollmer, F. Eyben, J. Keshet, A. Graves, B. Schuller, and G. Rigoll, “Robust discriminative keyword spotting for emotionally colored spontaneous speech using bidirectional LSTM networks,” in Proc. of ICASSP, Taipei, Taiwan, 2009.

How to Cite
Parwej, Y. (2015). The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents. Transactions on Machine Learning and Artificial Intelligence, 3(1), 16.