Abstract
Due to the advancement of technology, handwriting recognition has become more important than ever. As a result, several methods for document image recognition have been developed in the literature. This paper presents a new ensemble model based on the Feedforward Neural Networks (FFNN) to accurately recognize Persian and Arabic handwritten characters. As training and optimizing FFNN models have a significant role in obtaining optimal results, two optimization algorithms are integrated into the proposed handwritten recognition method. The Particle Swarm Optimization algorithm is integrated into the proposed model to improve the Neural Networks learning process. The FFNN architectures are further optimized using the League Championship Algorithm. The ensemble model is fed by a set of handcrafted features, including directional and intersection features, extracted from handwritten text. The proposed model is evaluated using three different datasets. Results obtained from the proposed models demonstrate higher accuracies compared to the state-of-the-art models.
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References
Elleuch, M., Jraba, S., Kherallah, M.: The effectiveness of transfer learning for arabic handwriting recognition using deep CNN. J. Inf. Assur. Secur. 16(2) (2021)
Alaei, A., Nagabhushan, P., Pal, U.: A new two-stage scheme for the recognition of persian handwritten characters. In: Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition (2010)
Farlinda, S., et al.: Application of backpropagation algorithm for handwriting recognition. In: Journal of Physics: Conference Series (2021)
Tanvir Parvez, M., Mahmoud, S.A.: Arabic handwriting recognition using structural and syntactic pattern attributes. Pattern Recognit. 46(1), 141–154 (2013)
Halavati, R., Shouraki, S.B.: Recognition of Persian online handwriting using elastic fuzzy pattern recognition. Int. J. Pattern Recognit. Artif. Intell. 21(03), 491–513 (2007)
Shahmoradi, S., Bagheri Shouraki, S.: Evaluation of a novel fuzzy sequential pattern recognition tool (fuzzy elastic matching machine) and its applications in speech and handwriting recognition. Appl. Soft Comput. 62, 315–327 (2018)
Mersa, O., Etaati, F., Masoudnia, S., Araabi, B.N.: Learning representations from Persian handwriting for offline signature verification, a deep transfer learning approach. In: Proceedings of the 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) (2019)
Safarzadeh, V.M., Jafarzadeh, P.: Offline Persian handwriting recognition with CNN and RNN-CTC. In: Proceedings of the 25th International Computer Conference (2020)
Mowlaei, A., Faez, K.: Recognition of isolated handwritten Persian/Arabic characters and numerals using support vector machines. In: Proceedings of the IEEE XIII Workshop on Neural Networks for Signal Processing (2003)
Manocha, S.K., Tewari, P.: Deep learning approaches for Devanagari handwriting recognition. In: Proceedings of the 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (2021)
Retsinas, G., Sfikas, G., Nikou, C.: Iterative weighted transductive learning for handwriting recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12824, pp. 587–601. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86337-1_39
Husseinzadeh Kashan, A.: League championship algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl. Soft Comput. 16, 171–200 (2014)
Ruiz-Parrado, V., Heradio, R., Aranda-Escolastico, E., Sánchez, Á., Vélez, J.F.: A bibliometric analysis of off-line handwritten document analysis literature (1990–2020). Pattern Recognit. 125 (2022)
Mozaffari, S., Soltanizadeh, H.: ICDAR handwritten Farsi/Arabic character recognition competition. In: Proceedings of the10th ICDAR, pp.1413–1417 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, IV. pp. 1942–1948 (1995)
Mozaffari, S., Faez, K., Faradji, F., Ziaratban, M., Golzan. S.M.: A comprehensive isolated Farsi/Arabic character database for handwritten OCR research. In: Proceedings of the10th IWFHR, pp. 385–389 (2006)
Razavi, S.M., Kabir, E.: A data base for online Persian handwritten recognition. In: Proceedings of the 6th Conference on Intelligent Systems, Kerman (2004)
Guo, H., Liu, Y., Yang, D., Zhao, J.: Offline handwritten Tai Le character recognition using ensemble deep learning. Vis. Comput. 38, 1–14 (2021). https://doi.org/10.1007/s00371-021-02230-2
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Motamedisedeh, O., Zagia, F., Alaei, A. (2023). A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Handwritten Character Recognition. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14194. Springer, Cham. https://doi.org/10.1007/978-3-031-41501-2_13
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