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Deep Neural Networks Features for Arabic Handwriting Recognition

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Advanced Information Technology, Services and Systems (AIT2S 2017)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 25))

Abstract

This work aims to compare the learning features with Convolutional Neural Networks (CNN) and the handcrafted features. In order to determine which the best between these two type of features. We consider our previous baseline HMM system [1] for Arabic handwritten word recognition. Experiments have been conducted on the well-known IFN/ENIT database. Achieved results using CNN features are better than those obtained by the hand-crafted features. This demonstrates the high efficiency of CNN results from the strong capability for hierarchical feature learning given a large amount of data. However, Hand-engineered features are not generated from an optimization process to be compatible with the specific problem, and insufficient to be encoded with supervision.

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References

  1. Rabi, M., Amrouch, M., Mahani, Z., Mammass, D.: Recognition of cursive Arabic handwritten text using embedded training based on HMMs. In: Engineering & MIS (ICEMIS, INSPEC Accession Number: 16467172. IEEE (2016). doi:10.1109/ICEMIS.2016.7745330

  2. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (2012)

    Google Scholar 

  3. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, S., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Li, F.F.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  4. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  5. LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: International Symposium on Circuits and Systems, pp. 253–256 (2010)

    Google Scholar 

  6. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  7. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, vol. 2011, p. 4 (2011)

    Google Scholar 

  8. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  9. Albeahdili, H.M., Alwzwazy, H.A., Islam, N.E.: Robust convolutional neural networks for image recognition. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 6(11) (2015)

    Google Scholar 

  10. Kaiming, H., Xiangyu, Z., Shaoqing, R., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition European. In: Conference on Computer Vision. arXiv:1406.4729v4 [cs.CV] (2015)

  11. Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: The International Joint Conference on In Neural Networks (IJCNN), pp. 2809–2813. IEEE (2011)

    Google Scholar 

  12. Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Proces. Mag. 29(6), 82–97 (2012). IEEE

    Article  Google Scholar 

  13. Sharma, A., PramodSankar, K.: Adapting off-the-shelf CNNs for word spotting & recognition. In: International Conference on Document Analysis and Recognition, pp. 986–990 (2015)

    Google Scholar 

  14. Simard, P.Y., Steinkraus, D., Platt, J.C. Best practices for convolutional neural networks applied to visual document analysis. In: International Conference Document Analysis and Recognition, pp. 958–962 (2003)

    Google Scholar 

  15. Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)

    MATH  Google Scholar 

  16. Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)

    Google Scholar 

  17. Couprie, C., Farabet, C., Najman, L., LeCun, Y.: Indoor semantic segmentation using depth information. In: International Conference on Learning Representation (2013)

    Google Scholar 

  18. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311.2524 (2013)

    Google Scholar 

  19. Ciresan, D., Meier, U., Masci, J., Schmidhuber, J.: A committee of neural networks for traffic sign classification. In: The 2011 International Joint Conference on in Neural Networks (IJCNN), pp. 1918–1921. IEEE (2011)

    Google Scholar 

  20. LeCun, Y., Bottou, L., Bengio, Y.: Reading checks with multilayer graph transformer networks. In: International Conference on Acoustics, Speech, and Signal Processing (1997)

    Google Scholar 

  21. Bluche, T., Ney, H., Kermorvant, C.: Tandem HMM with convolutional neural network for handwritten word recognition. In: 38th International Conference on Acoustics Speech and Signal Processing (ICASSP2013), pp. 2390–2394 (2013)

    Google Scholar 

  22. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:1406.2227 (2014)

  23. Yuan, G.B., Jiao, L., Liu, Y.: Offline handwritten English character recognition based on convolutional neural network. In: 10th IAPR International Workshop on Document Analysis Systems (DAS), pp. 125–129 (2012). doi:10.1109/DAS.2012.61

  24. Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. In: ICLR (2014)

    Google Scholar 

  25. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep big simple neural nets excel on handwritten digit recognition, CoRR, abs/1003.0358 (2010)

    Google Scholar 

  26. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: International Conference of Document Analysis and Recognition, vol. 10, pp. 1135–1139 (2011)

    Google Scholar 

  27. Cireşan, D., Schmidhuber, J.: Multi-column deep neural networks for offline handwritten Chinese character classification. arXiv preprint arXiv: 1309.0261 (2013)

    Google Scholar 

  28. Graham, B.: Sparse arrays of signatures for online character recognition. arXiv:1308.0371 (2013)

  29. Yin, F., Wang, Q.F., Zhang, X.Y., et al.: ICDAR 2013 chinese handwriting recognition competition. In: Proceedings 12th International Conference Document Analysis and Recognition, pp. 1464–1470 (2013)

    Google Scholar 

  30. Parvez, M.T., Mahmoud, S.A.: Offline Arabic handwritten text recognition: A survey. ACM Comput. Surv. 45(2), 23–35 (2013)

    Article  MATH  Google Scholar 

  31. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Le-Cun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. CoRR (2013)

    Google Scholar 

  32. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR, vol. abs/1409.4842 (2014)

    Google Scholar 

  33. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Technical report (2014)

    Google Scholar 

  34. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. https://arxiv.org/abs/1512.03385 (2015)

  35. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)

    Google Scholar 

  36. El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Combining slanted-frame classifiers for improved HMM-based Arabic handwriting recognition. IEEE PAMI 31(7), 1165–1177 (2009)

    Article  Google Scholar 

  37. Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 41(1), 164–171 (1970). http://dx.doi.org/10.1214/aoms/1177697196

    Article  MATH  MathSciNet  Google Scholar 

  38. Forney Jr., G.D.: The Viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973). http://dx.doi.org/10.1109/PROC.1973.9030

    Article  MathSciNet  Google Scholar 

  39. Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: JMLR W & CP, vol. 28(3), pp. 1139–1147 (2013)

    Google Scholar 

  40. Keras (2016). https://github.com/fchollet/keras

  41. Young, S., et al.: The HTK Book V3.4. Cambridge University Press, Cambridge (2006)

    Google Scholar 

  42. Irfane, A., Fink, G., Mahmoud, S., et al.: Improvements in sub-character hmm model based arabic text recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 537–542. IEEE (2014)

    Google Scholar 

  43. Alkhateeb, J.H., Ren, J., Jiang, J., Al-Muhtaseb, H.: Offline handwritten arabic cursive text recognition using hidden markov models and re-ranking. Pattern Recogn. Lett. 32, 1081–1088 (2011)

    Article  Google Scholar 

  44. Maqqor, A., Halli, A., Satori, K., Tairi, H.: Off-line recognition Handwriting combination of multiple classifiers. In: 3rd International IEEE Colloquium on Information Science and Technology, IEEE CIST 2014 (2014)

    Google Scholar 

  45. El Moubtahij, H., Akram, H., Satori, K.: Using features of local densities, statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition (2016)

    Google Scholar 

  46. Jayech, K., Mahjoub, M.A., Amara, N.B.: Arabic handwritten word recognition based on dynamic bayesian network (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory IRF-SIC Faculty of Sciences, Ibn Zohr University, Agadir, Morocco

    Mustapha Amrouch & Mouhcine Rabi

Authors
  1. Mustapha Amrouch
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  2. Mouhcine Rabi
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Correspondence to Mustapha Amrouch .

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Editors and Affiliations

  1. Faculty of Sciences and Technologies, Tangier, Morocco

    Mostafa Ezziyyani

  2. Faculty of Sciences and Technologies, University Hassan 1st, Settat, Morocco

    Mohamed Bahaj

  3. Faculty of Sciences and Technologies, Mohammedia, Morocco

    Faddoul Khoukhi

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Amrouch, M., Rabi, M. (2018). Deep Neural Networks Features for Arabic Handwriting Recognition. In: Ezziyyani, M., Bahaj, M., Khoukhi, F. (eds) Advanced Information Technology, Services and Systems. AIT2S 2017. Lecture Notes in Networks and Systems, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-69137-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-69137-4_14

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  • Online ISBN: 978-3-319-69137-4

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