Abstract This work presents survey, implementation and test for a neural network: Time Delay Neural Network (TDNN), applied to on-line handwritten recognition characters. In this work, we present a recognizer conception for on-line Arabic handwriting. On-line handwriting recognition of Arabic script is a complex problem, since it is naturally both cursive and unconstrained. This system permits to interpret a script represented by the pen trajectory. This technique is used notably in the electronic tablets. We will construct a data base with several scripters. Afterwards, and before attacking the recognition phase, there is a constructional samples phase of Arabic characters acquired from an electronic tablet to digitize Noun Database. Obtained scores shows an effectiveness of the proposed approach based on convolutional neural networks.

References

[1] Jayech K., Mahjoub M., and Amara N., “Synchronous Multi-Stream Hidden Markov Model for Offline Arabic Handwriting Recognition without Explicit Segmentation,” Neurocomputing, vol. 214, pp. 958-971, 2016.

[2] Jin C., He B., Hui K., and Sun L., “TDNN: A Two-Stage Deep Neural Network for Prompt- Independent Automated Essay Scoring,” in Proceedings of the 56thAnnual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, pp. 1088- 1097, 2018.

[3] Kim D. and Cho J., “Improvement of Anomalous Behavior Detection of GNSS Signal Based on TDNN for Augmentation Systems,” Sensors, vol. 18, no. 11, pp. 3800, 2018.

[4] Ltaief M., Bezine H., and Alimi A., “A Spiking Neural Network Model for Complex Handwriting Movements Generation,” International Journal of Computer Science and Information Security, vol. 14, no. 7, pp. 319, 2016. 524 The International Arab Journal of Information Technology, Vol. 16, No. 3A, Special Issue 2019

[5] Mandal S., Prasanna S., and Sundaram S., “GMM Posterior Features for Improving Online Handwriting Recognition,” Expert System with Applications, vol. 97, pp. 421-433, 2018.

[6] Marukatat S., Artieres T., Dorizzi B., and Gallinari P., “Sentence Recognition Through Hybrid Neuro-Markovian Modeling,” in Proceedings of the 6thInternational Conference on Document Analysis and Recognition, Seattle, pp. 731-735, 2001.

[7] Prabhakar S. and Rajaguru H., Computational Intelligence, Cyber Security and Computational Models, Springer, 2016.

[8] Priya A., Mishra S., Raj S., Mandal S., and Datta S., “Online and Offline Character Recognition: A Survey,” in Proceedings of the International Conference on Communication and Signal Processing, Melmaruvathur, pp. 967-970, 2016.

[9] Tasdemir E. and Yanikoglu B., “A Comparative Study of Delayed Stroke Handling Approaches in Online Handwriting,” International Journal on Document Analysis and Recognition, vol. 22, no. 1, pp. 15-28, 2018.

[10] Tlemsani R. and Benyettou A., “Application des Réseaux Bayésiens Dynamiques à La Reconnaissance En-Ligne des Caractères Isolés,” in Proceedings of the 4thInternational Conference Sciences of Electronic, Technologies of Information and Telecommunications, Tunisia, pp. 1-8, 2007.

[11] Tlemsani R. and Benyettou A., “Improved Dynamic Bayesian Networks Applied to Arabic on Line Characters Recognition,” World Academy of Science, Engineering and Technology, vol. 8, no. 4, pp. 600-605, 2014.

[12] Tlemsani R. and Benyettou A., “On Line Isolated Characters Recognition Using Dynamic Bayesian Networks,” the International Arab Journal of Information Technology, vol. 8, no. 4, pp. 406- 413, 2011.

[13] Tyasnurita R., Özcan E., and John R., “Learning Heuristic Selection Using A Time Delay Neural Network for Open Vehicle Routing,” in Proceedings of IEEE Congress on Evolutionary Computaion, San Sebastian, pp. 1474-1481, 2017.

[14] Zouari R., Boubaker H., and Kherallah M., “Multi-Language Online Handwriting Recognition Based on Beta-Elliptic Model and Hybrid TDNN-SVM Classifier,” Multimedia Tools and Applications, vol. 78, no. 9, pp. 12103- 12123, 2019.

[15] Zouari R., Boubaker H., and Kherallah M., “Hybrid TDNN-SVM Algorithm for Online Arabic Handwriting Recognition,” in Proceedings of International Conference on Hybrid Intelligent Systems, Seoul, pp. 113-123, 2016. Khadidja Belbachir is actually a PhD in pattern recognition and Data Mining, young researcher in data mining and association rules and master’s in data processing and computer sciences from the university of Sciences and Technologies of Oran USTO, Algeria. She also teaches modules at both Engineering and LMD levels in computer science and software engineering. His current research interests are in the area of Handwriting recognition, cloud computing, Data bases engineering, data mining and big data. Redouane Tlemsani is associate professor and PhD in Arabic Handwriting Recognition, young researcher in pattern recognition and artificial intelligence and master’s in data processing and computer sciences from the university of Sciences and Technologies of Oran USTO, Algeria. He is a member of the research team of laboratory SIMPA since 2002. He also teaches modules at both Engineering and LMD levels in computer science and software engineering.He is actually at the National institute of Telecommunications, Information Technologies and Communication of Oran - INTTIC, Algeria.