Abstract
This paper presents an analytical approach for offline Arabic Handwritten Text Recognition (HTR), based on Convolutional Recurrent Neural Network (CRNN). The suggested method is a three-part end-to-end trainable deep learning system that includes feature extraction, label prediction, and transcription part. The first part is performed by Convolutional Neural Network (CNN) layers, where sequential features are extracted. In the label prediction part, the extracted features are used to generate new sequential contextual features by feeding them to recurrent layers. This set of features for Arabic texts is then used to predict label distributions with fully connected layers. In the third part of the system, the transcription part, the predicted label distributions are translated into actual label sequences, using the Connectionist Temporal Classification (CTC) method. The experiments are carried out and reported on the publicly available IFN/ENIT database. The results of the proposed system are encouraging, and the recognition rates are comparable to those of numerous other systems in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akram, H., Khalid, S., et al.: Using features of local densities, statistics and hmm toolkit (HTK) for offline Arabic handwriting text recognition. J. Electr. Syst. Inf. Technol. 4(3), 387–396 (2017)
Al Abodi, J., Li, X.: An effective approach to offline Arabic handwriting recognition. Comput. Electr. Eng. 40(6), 1883–1901 (2014)
AlKhateeb, J.H., Pauplin, O., Ren, J., Jiang, J.: Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowl.-Based Syst. 24(5), 680–688 (2011)
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(8), 1081–1088 (2011)
Amrouch, M., Rabi, M., Es-Saady, Y.: Convolutional feature learning and CNN based HMM for Arabic handwriting recognition. In: Mansouri, A., El Moataz, A., Nouboud, F., Mammass, D. (eds.) ICISP 2018. LNCS, vol. 10884, pp. 265–274. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94211-7_29
Burkhard, W.A., Keller, R.M.: Some approaches to best-match file searching. Commun. ACM 16(4), 230–236 (1973)
El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Arabic handwriting recognition using baseline dependant features and hidden Markov modeling. In: Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 893–897. IEEE (2005)
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: S. Choras, R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol. 233, pp. 245–254. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-01622-1_29
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 (2006)
Hamdani, M., El Abed, H., Kherallah, M., Alimi, A.M.: Combining multiple HMMs using on-line and off-line features for off-line Arabic handwriting recognition. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 201–205. IEEE (2009)
Jayech, K., Mahjoub, M.A., Amara, N.E.B.: Arabic handwritten word recognition based on dynamic Bayesian network. Int. Arab J. Inf. Technol. 13(6B), 1024–1031 (2016). http://iajit.org/index.php?option=com_content&task=blogcategory&id=104&Itemid=387
Jayech, K., Mahjoub, M.A., Amara, N.E.B.: Synchronous multi-stream hidden Markov model for offline Arabic handwriting recognition without explicit segmentation. Neurocomputing 214, 958–971 (2016)
Kessentini, Y., Paquet, T., Hamadou, A.B.: Off-line handwritten word recognition using multi-stream hidden Markov models. Pattern Recogn. Lett. 31(1), 60–70 (2010)
Lee, C.Y., Osindero, S.: Recursive recurrent nets with attention modeling for OCR in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2231–2239 (2016)
Metwally, A.H., Khalil, M.I., Abbas, H.M.: Offline Arabic handwriting recognition using hidden Markov models and post-recognition lexicon matching. In: 2017 12th International Conference on Computer Engineering and Systems (ICCES), pp. 238–243. IEEE (2017)
Parvez, M.T., Mahmoud, S.A.: Arabic handwriting recognition using structural and syntactic pattern attributes. Pattern Recogn. 46(1), 141–154 (2013)
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H., et al.: IFN/ENIT-database of handwritten Arabic words. In: Proceedings of CIFED, vol. 2, pp. 127–136. Citeseer (2002)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2016)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS (2014)
Yousif, I., Shaout, A.: Off-line handwriting arabic text recognition: a survey. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(9) (2014)
Acknowledgments
We thank Dr. Volker Märgner for sharing the IFN/ENIT database with us.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chadli, M.A., Bachir Bouiadjra, R., Fekir, A. (2023). Offline Arabic Handwritten Text Recognition for Unsegmented Words Using Convolutional Recurrent Neural Network. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-28540-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28539-4
Online ISBN: 978-3-031-28540-0
eBook Packages: Computer ScienceComputer Science (R0)