Abstract
Hidden Markov models (HMMs) are now widely used for off-line handwriting recognition in many languages and, in particular, in Arabic. As in speech recognition, they are usually built from shared, embedded HMMs at the symbol level, in which state-conditional probability density functions are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kinds of features should be used and, indeed, very different feature sets are in use today. Among them, we have recently proposed to simply use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea is to bypass feature extraction and ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this chapter, we review this idea along with some extensions that are currently providing state-of-the-art results on Arabic handwritten word recognition.
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References
Dreuw, P., Heigold, G., Ney, H.: Confidence-based discriminative training for model adaptation in offline Arabic handwriting recognition. In: ICDAR, pp. 596–600 (2009)
Giménez, A., Juan, A.: Embedded Bernoulli mixture HMMs for handwritten word recognition. In: Proc. of the 10th Int. Conf. on Document Analysis and Recognition (ICDAR 2009), Barcelona, Spain, pp. 896–900 (2009)
Giménez, A., Khoury, I., Juan, A.: Windowed Bernoulli mixture HMMs for Arabic handwritten word recognition. In: Proc. of the Int. Conf. on Frontiers in Handwriting Recognition (ICFHR 2010), Kolkata, India (2010)
Goodman, J.T.: A bit of progress in language modeling. Tech. rep. (2001)
Khoury, I., Giménez, A., Juan, A.: Arabic handwritten word recognition using Bernoulli mixture HMMs. In: Proc. of the 3rd Palestinian Int. Conf. on Computer and Information Technology (PICCIT 2010), Hebron, Palestine (2010)
Lorigo, L., Govindaraju, V.: Offline Arabic handwriting recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 712–724 (2006). doi:10.1109/TPAMI.2006.102
Märgner, V., Abed, H.E.: ICDAR 2007—Arabic handwriting recognition competition. In: Proc. of the 9th Int. Conf. on Document Analysis and Recognition (ICDAR 2007), Curitiba, Brazil, vol. 2, pp. 1274–1278 (2007)
Märgner, V., Abed, H.E.: ICDAR 2009 Arabic handwriting recognition competition. In: Proc. of the 10th Int. Conf. on Document Analysis and Recognition (ICDAR 2009), Barcelona, Spain, pp. 1383–1387 (2009)
Märgner, V., Pechwitz, M., Abed, H.E.: ICDAR 2005 Arabic handwriting recognition competition. In: Proc. of the 8th Int. Conf. on Document Analysis and Recognition (ICDAR 2005), Seoul, Korea, vol. 1, pp. 70–74 (2005)
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H.: IFN/ENIT—database of handwritten Arabic words. In: 7th Colloque International Francophone sur l’Ecrit et le Document, CIFED, Hammamet, Tunis, pp. 21–23 (2002)
Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall, Englewood Cliffs (1993)
Young, S., et al.: The HTK Book. Cambridge University Engineering Department, Cambridge (1995)
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Alkhoury, I., Giménez, A., Juan, A. (2012). Arabic Handwriting Recognition Using Bernoulli HMMs. In: Märgner, V., El Abed, H. (eds) Guide to OCR for Arabic Scripts. Springer, London. https://doi.org/10.1007/978-1-4471-4072-6_10
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DOI: https://doi.org/10.1007/978-1-4471-4072-6_10
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