Abstract
In recent years Deep Neural Networks (DNNs) have been successfully applied to several pattern recognition filed. For example, Multi-Column Deep Neural Networks (MCDNN) achieve state of the art recognition rates on Chinese characters database. In this paper, we utilized MCDNN for Offline Arabic Handwriting Recognition (OAHR). Through several settings of experiments using the benchmarking IFN/ENIT Database, we show incremental improvements of the words recognition comparable to approaches used Deep Belief Network (DBN) or Recurrent Neural Network (RNN.) Lastly, we compare our best result to those of previous state-of-the-arts.
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References
Kim, K.K., Jin, H.K., Yun, K.C., et al.: Legal amount recognition based on the segmentation hypotheses for bank check processing. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 964–967. IEEE Xplore (2001)
Vinciarelli, A.: A survey on off-line cursive word recognition. Pattern Recogn. 35(7), 1433–1446 (2002)
Mohamad, A.H., Likformansulem, L., Mokbel, C.: Combining slanted-frame classifiers for improved HMM-based Arabic handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1165–1177 (2009)
Ali, M.A.: Arabic handwritten characters classification using learning vector quantization algorithm. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 463–470. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69905-7_53
Elzobi, M., Al-Hamadi, A., Al Aghbari, Z., Dings, L., Saeed, A.: Gabor wavelet recognition approach for off-line handwritten arabic using explicit segmentation. In: Choras, R.S. (ed.) Image Processing and Communications Challenges 5, pp. 245–254. Springer, Heidelberg (2014). doi:10.1007/978-3-319-01622-1_29
Khemiri, A., Kacem, A., Belaid, A.: Towards Arabic handwritten word recognition via probabilistic graphical models. In: International Conference on Frontiers in Handwriting Recognition, pp. 678–683. IEEE (2014)
Madhvanath, S., Govindaraju, V.: The role of holistic paradigms in handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 149–164 (2001)
Ruiz-Pinales, J., Jaime-Rivas, R., Castro-Bleda, M.J.: Holistic cursive word recognition based on perceptual features. Pattern Recogn. Lett. 28(13), 1600–1609 (2007)
Wu, C., Fan, W., He, Y., et al.: Handwritten character recognition by alternately trained relaxation convolutional neural network. In: International Conference on Frontiers in Handwriting Recognition, pp. 291–296. IEEE (2014)
Graves, A.: Offline Arabic handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 545–552 (2012)
Elleuch, M., Tagougui, N., Kherallah, M.: Deep learning for feature extraction of Arabic handwritten script. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9257, pp. 371–382. Springer, Cham (2015). doi:10.1007/978-3-319-23117-4_32
Cireşan, D., Meier, U., Masci, J., et al.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32(1), 333 (2012)
Schmidhuber, J., Meier, U., Ciresan, D.: Multi-column deep neural networks for image classification, vol. 157, no. 10, pp. 3642–3649 (2012)
Liu, C.L., Yin, F., Wang, Q.F., et al.: ICDAR 2011 Chinese handwriting recognition competition. In: International Conference on Document Analysis and Recognition, pp. 1464–1469. IEEE (2011)
Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: ICDAR 2013 (2013)
Dan, C., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: International Joint Conference on Neural Networks, pp. 1–6. IEEE (2015)
Aburas, A.A., Gumah, M.E.: Arabic handwriting recognition: challenges and solutions. In: International Symposium on Information Technology, pp. 1–6. IEEE (2008)
Srihari, S.N., Ball, G.: An assessment of arabic handwriting recognition technology. In: Märgner, V., El Abed, H. (eds.) Guide to OCR for Arabic Scripts, pp. 3–34. Springer, London (2012). doi:10.1007/978-1-4471-4072-6_1
Lecun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: International Symposium on Circuits and Systems, DBLP, pp. 253–256 (2010)
Maalej, R., Kherallah, M.: Improving MDLSTM for offline Arabic handwriting recognition using dropout at different positions. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 431–438. Springer, Cham (2016). doi:10.1007/978-3-319-44781-0_51
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Acknowledgments
This research was supported in part by Science & Technology Pillar Program of Hubei Province under Grant (#2014BAA146), Nature Science Foundation of Hubei Province under Grant (#2015CFA059), Science and Technology Open Cooperation Program of Henan Province under Grant (#152106000048).
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Almodfer, R., Xiong, S., Mudhsh, M., Duan, P. (2017). Multi-column Deep Neural Network for Offline Arabic Handwriting Recognition. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_30
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DOI: https://doi.org/10.1007/978-3-319-68612-7_30
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