nach oben

Cluster Computing

Erschienen in:

22.06.2017

Ligature based Urdu Nastaleeq sentence recognition using gated bidirectional long short term memory

verfasst von: Ibrar Ahmad, Xiaojie Wang, Yuz hao Mao, Guang Liu, Haseeb Ahmad, Rahat Ullah

Erschienen in: Cluster Computing | Ausgabe 1/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Bidirectional long short term memory (BLSTM) architecture—a special case of recurrent neural network—is successfully applied for recognition of Urdu Nastaleeq sentence images based on character information. In such cases, manual labeling of characters in sentences for a large dataset is an intensive job, because identical characters observe different shapes at different positions inside ligatures and words. On the other hand, labeling any dataset with ligatures is a relatively easier and more accurate phenomenon. In the current paper, we propose a novel gated BLSTM (GBLSTM) model for recognition of printed Urdu Nastaleeq text based on ligature information. Our proposed model incorporates raw pixel values as features instead of human crafted features, because of the latter being more error prone. The model is trained on un-degraded and tested on unseen artificially degraded versions of Urdu printed text images dataset. The recognition accuracy of the proposed GBLSTM model is 96.71% that is higher than the prevalent Urdu optical character recognition systems.

Vorheriger Artikel Extreme learning machine based microscopic red blood cells classification

Nächster Artikel User stories complexity estimation using Bayesian networks for inexperienced developers

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

UPTIs dataset is provided by faisal.shafait@uwa.edu.au and adnan@cs.uni-kl.de.

Naz, S., Umar, A.I., Shirazi, S.H., Khan, S.A., Ahmed, I., Khan, A.A.: Challenges of Urdu named entity recognition: a scarce resourced language. Res. J. Appl. Sci. Eng. Technol. 8(10), 1272–1278 (2014)

Daud, A., Khan, W., Che, D.: Urdu language processing: a survey. Artif. Intell. Rev. (2016). doi:10.1007/s10462-016-9482-x

Weber, G.: Top languages. World 10 (2008)

Naz, S., Hayat, K., Razzak, M.I., Anwar, M.W., Madani, S.A., Khan, S.U.: The optical character recognition of Urdu-like cursive scripts. Pattern Recognit. 47(3), 1229–1248 (2014)CrossRef

Javed, S.T., Hussain, S., Maqbool, A., Asloob, S., Jamil, S., Moin, H.: Segmentation free Nastalique Urdu OCR. World Acad. Sci. Eng. Technol. 46, 456–461 (2010)

Husain, S.A.: A multi-tier holistic approach for Urdu Nastaleeq recognition. In: International Multi-topic Conference, 2002. INMIC 2002. Abstracts, pp. 84–84. IEEE (2002)

Ahmad, I., Wang, X., Li, R., Rasheed, S.: Offline Urdu Nastaleeq optical character recognition based on stacked denoising autoencoder. China Commun. 14(1), 146–157 (2017)CrossRef

Sabbour, N., Shafait, F.: A segmentation-free approach to Arabic and Urdu OCR. IS&T/SPIE Electron. Imaging Int. Soc. Opt. Photon. (2013) doi:10.1117/12.2003731

Lehal, G.S., Rana, A.: Recognition of Nastalique Urdu ligatures. In: Proceedings of the 4th International Workshop on Multilingual OCR, p. 7. ACM, New York (2013)

10.

El-Korashy, A., Shafait, F.: Search space reduction for holistic ligature recognition in Urdu Nastalique script. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1125–1129. IEEE (2013)

11.

Hussain, D., Hassan, D., Guo, P.: Improved Arabic word classification using spatial pyramid matching method. In: International Conference Image and Vision Computing New Zealand (IVCNZ, 2011)

12.

Hussain, S., Ali, S., Akram, Q.: Nastalique segmentation-based approach for Urdu OCR. Int. J. Doc. Anal. Recognit. 18(4), 357–374 (2015)CrossRef

13.

Javed, S.T., Hussain, S.: Segmentation based Urdu Nastalique OCR. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 41–49. Springer, Berlin (2013)

14.

Ul-Hasan, A., Ahmed, S.B., Rashid, F., Shafait, F., Breuel, T.M.: Offline printed Urdu Nastaleeq script recognition with bidirectional LSTM networks. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1061–1065. IEEE (2013)

15.

Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., Razzak, M.I.: Urdu Nastaliq text recognition system based on multidimensional recurrent neural network and statistical features. Neural Comput. Appl. (2015). doi:10.1007/s00521-015-2051-4

16.

Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., Siddiqi, I., Razzak, M.I.: Offline cursive Urdu-Nastaliq script recognition using multidimensional recurrent neural networks. Neurocomputing (2015). doi:10.1016/j.neucom.2015.11.030

17.

Ahmed, S.B., Naz, S., Razzak, M.I., Rashid, S.F., Afzal, M.Z., Breuel, T.M.: Evaluation of cursive and non-cursive scripts using recurrent neural networks. Neural Comput. Appl. (2015). doi:10.1007/s00521-015-1881-4

18.

Naz, S., Ahmed, S.B., Ahmad, R., Razzak, M.I.: Zoning features and 2DLSTM for Urdu text-line recognition. Procedia Comput. Sci. 96, 16–22 (2016)CrossRef

19.

Naz, S., Umar, A.I., Ahmad, R., Razzak, M.I., Rashid, S.F., Shafait, F.: Urdu Nasta’liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks. SpringerPlus 5(1), 2010 (2016)CrossRef

20.

Naz, S., Umar, A.I., Ahmad, R., Siddiqi, I., Ahmed, S.B., Razzak, M.I., Shafait, F.: Urdu Nastaliq recognition using convolutional-recursive deep learning. Neurocomputing 243, 80–87 (2017)CrossRef

21.

Graves, A.: Supervised Sequence Labelling. Springer, Berlin (2012)CrossRefMATH

22.

Satti, D.A., Saleem, K.: Complexities and implementation challenges in offline Urdu Nastaliq OCR. In: Proceedings of the Conference on Language and Technology, pp. 85–91 (2012)

23.

Sarfraz, H., Dilawari, A., Hussain, S.: Assessing Urdu language support on the multilingual web. In: Proceedings of the 12th AMIC Annual Conference on e-Worlds: Governments, Business and Civil Society. Asian Media Information Center, Singapore (2003)

24.

Bridle, J.S.: Probabilistic interpretation of feed forward classification network outputs, with relationships to statistical pattern recognition. Neurocomputing (1990). doi:10.1007/978-3-642-76153-9_28

25.

Canyameres Masip, S., López Peña, A.M., et al.: On the use of convolutional neural networks for pedestrian detection (2015)

26.

Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)CrossRef

27.

Wu, D.: Human action recognition using deep probabilistic graphical models. PhD Thesis, University of Sheffield (2014)

28.

Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM, New York (2008)

29.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefMATH

30.

Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)CrossRef

31.

Jaeger, H.: Tutorial on Training Recurrent Neural Networks, Covering BPPT, RTRL, EKF and the “echo state network” Approach. GMDForschungszentrum Informationstechnik (2002)

32.

Senior, A., Robinson, T.: Forward–backward retraining of recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 743–749 (1996)

33.

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

34.

Graves, A.: Offline Arabic handwriting recognition with multidimensional recurrent neural networks. In: Guide to OCR for Arabic Scripts, pp. 297–313. Springer, London (2012)

35.

Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. Adv. Neural Inf. Process. Syst. 19, 545–552 (2009)

36.

Graves, A., Fernández, S., Schmidhuber, J.: Bidirectional LSTM networks for improved phoneme classification and recognition. In: International Conference on Artificial Neural Networks, pp. 799–804. Springer, Berlin (2005)

37.

Graves, A., Liwicki, M., Bunke, H., Schmidhuber, J., Fern’andez, S.: Unconstrained on-line handwriting recognition with recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 577–584 (2008)

38.

Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)CrossRef

39.

Wojna, Z.: Fast methods in training deep neural networks for image recognition. PhD Thesis, University College London (2015)

40.

Lian, Z., Jing, X., Wang, X., Huang, H., Tan, Y., Cui, Y.: DropConnect regularization method with sparsity constraint for neural networks. Chin. J. Electron. 25(1), 152–158 (2016)CrossRef

41.

Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics (AISTATS-11) (G.J. Gordon and D.B. Dunson, eds.). J. Mach. Learn. Res. Workshop Conf. Proc. 15, 315–323 (2011)

42.

Baird, H.S.: Document image defect models. In: Structured Document Image Analysis, pp. 546–556. Springer, New York (1992)

43.

Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 369–376. ACM, New York (2006)

44.

Ahmad, I., Wang, X., Li, R., Ahmad, M., Ullah, R.: Line and ligature segmentation of Urdu Nastaleeq text. IEEE Access (2017). doi:10.1109/ACCESS.2017.2703155

Titel: Ligature based Urdu Nastaleeq sentence recognition using gated bidirectional long short term memory
verfasst von: Ibrar Ahmad
Xiaojie Wang
Yuz hao Mao
Guang Liu
Haseeb Ahmad
Rahat Ullah
Publikationsdatum: 22.06.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 1/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-0990-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2018

Facial expression recognition using histogram of oriented gradients based transformed features

Mining biometric data to predict programmer expertise and task difficulty

A new efficient authorized private set intersection protocol from Schnorr signature and its applications

Trajectory based vehicle counting and anomalous event visualization in smart cities

Enhancement of data confidentiality and secure data transaction in cloud storage environment

Novel power reduction framework for enhancing cloud computing by integrated GSNN scheduling method