Abstract
In this study, we propose a synchronous Multi-Stream Hidden Markov Model (MSHMM) for offline Arabic handwriting word recognition. Our proposed model has the advantage of efficiently modelling the temporal interaction between multiple features. These features are composed of a combination of statistical and structural ones, which are extracted over the columns and rows using a sliding window approach. In fact, word models are implemented based on the holistic and analytical approaches without any explicit segmentation. In the first approach, all the words share the same architecture but the parameters are different. Nevertheless, in the second approach, each word has it own model by concatenating its character models. The results carried out on the IFN/ENIT database show that the analytical approach performs better than the holistic one and the MSHMMs in Arabic handwriting recognition is reliable.
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References
Giménez, A., Khoury, I., Andrés-Ferrer, J., Juan, A.: Handwriting word recognition using windowed Bernoulli HMMs. Patt. Recog. Lett. 35, 149–156 (2014)
El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Combining slanted-frame classifiers for improved HMM-based Arabic handwriting recognition. IEEE Trans. Patt. Anal. Mach. Intell. 31, 1165–1177 (2009)
Kessentini, Y., Paquet, T., Hamadou, A.B.: Off-line handwritten word recognition using multi-stream hidden Markov models. Patt. Recog. Lett. 31, 60–70 (2010)
Menasri, F., Vincent, N., Augustin, E., Cheriet, M.: Un système de reconnaissance de mots Arabes manuscrits hors ligne sans signes diacritiques. In: Proceeding of CIFED, pp.121–126, 2008
Jayech, K., Mahjoub, M.A., Essoukri Ben Amara, N.: Arabic handwritten word recognition based on dynamic bayesian network. Int. Arab J. Inform. Technol. (IAJIT) 13(3) (2016) (To appear)
Jayech, K., Trimech, N., Mahjoub, M.A., Essoukri Ben Amara, N.: Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition. In: IEEE 4th International Conference on ICT & Accessibility, 24–26 Oct 2013
Mahjoub, M.A., Ghanmy, N., Jayech, K., Miled, I.: Multiple models of Bayesian networks applied to offline recognition of Arabic handwritten city names. Int. J. Imaging Robot. 9(1) (2013)
Rabiner, LR.: A tutorial on hidden Markov model and selected applications in speech recognition. In: Readings in Speech Recognition, pp. 267–296, San Mateo (1990)
Elbaati, A., Kherallah, M., El Abed, H., Ennaji, A., Alimi, A.M.: Arabic handwriting recognition using restored stroke chronology. In: Proceeding of ICDAR, (2009)
Dreuw, P., Heigold, G., Ney, H.: Confidence-based discriminative training for model adaptation in offline Arabic handwriting recognition. In: Proceeding of ICDAR (2009)
Abandah, G., Jamour, F.: Recognizing handwritten Arabic script through efficient skeleton-based grapheme segmentation algorithm. In: Proceedings of ISDA, pp. 977–982 (2010)
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Jayech, K., Mahjoub, M.A., Amara, N.E.B. (2015). Arabic Handwriting Recognition Based on Synchronous Multi-stream HMM Without Explicit Segmentation. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_12
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DOI: https://doi.org/10.1007/978-3-319-19644-2_12
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