Comparison of Handwritten Recognition Methods on Arabic and Latin Characters
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Abstract
In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 testings) were used. When the results of the experiment are examined, it is seen that successful results are obtained in the classification made with the MADBase dataset in some methods and in the classification made with the MNIST dataset in some methods. As a result, it can be stated that the handwriting character recognition success of a method cannot be measured only by the classification made on a dataset. Also, the digits written in the Arabic alphabet appear to be almost more recognizable than the digits written in the Latin alphabet.
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References
Grimsdale, R. L., Sumner, F. H., Tunis, C. J., & Kilburn, T., 1959. A system for the automatic recognition of patterns. Proceedings of the IEE-Part B: Radio and Electronic Engineering, 106(26), 210-221.
Megha Agarwal, Shalika, Vinam Tomar, Priyanka Gupta., 2019. “Handwritten Character Recognition using Neural Network and Tensor Flow”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue- 6S4.
Hull, J. J., 1994. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine intelligence, 16(5), 550-554.
Grother, P. J., 1995. Nist special database 19-hand-printed forms and characters database. Technical Report, National Institute of Standards and Technology.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. http://yann.lecun.com/exdb/mnist/
AlKhateeb, J. H., Ren, J., Ipson, S., and Jiang, J., 2008. Knowledge-based baseline detection and optimal thresholding for words segmentation in efficient preprocessing of handwritten Arabic text. Fifth international conference on information technology: new generations. IEEE computer society. pp. 1158-1159.
Khorsheed, M.S., 2002. Off-line Arabic character recognition a review. Pattern Analysis & Applications. 5(1): 31-45.
Liana, M., and Venu, G., 2006. Offline Arabic Handwriting Recognition: A Survey. IEEE, Transactions on Pattern Analysis and Machine Intelligence. 28: 712-724.
Al-Ohali, Y., Cheriet, M., & Suen, C., 2003. Databases for recognition of handwritten Arabic cheques. Pattern Recognition, 36(1), 111-121.
El-Sherif, E. A., & Abdelazeem, S., 2007. A Two-Stage System for Arabic Handwritten Digit Recognition Tested on a New Large Database. In Artificial intelligence and pattern recognition (pp. 237-242).
Mahmoud, S. A., Ahmad, I., Alshayeb, M., Al-Khatib, W. G., Parvez, M. T., Fink, G. A., ... & El Abed, H., 2012. Khatt: Arabic offline handwritten text database. In 2012 International conference on frontiers in handwriting recognition (pp. 449-454). IEEE.
Jbrail, M.W. and Tenekeci, M.E., 2022. Character Recognition of Arabic Handwritten Characters Using Deep Learning. Journal of Studies in Science and Engineering, 2(1), pp.32-40.
Bartos, G. E., Hoşcan, Y., Kauer, A., & Hajnal, É. N., 2020. A Multilingual Handwritten Character Dataset: THE Dataset. Acta Polytechnica Hungarica, 17(9).
Marti, U. V., & Bunke, H., 2002. The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46.
Wu, H., 2018. CNN-Based Recognition of Handwritten Digits in MNIST Database. Research School of Computer Science. The Australia National University, Canberra.
Peterson, L. E., 2009. K-nearest neighbor. Scholarpedia, 4(2), 1883.
Karakaya, R., 2020. Makine öğrenmesi yöntemleriyle el yazısı tanıma (Master's thesis, Sakarya Üniversitesi).
Bharadwaj, Y. S., Rajaram, P., Sriram, V. P., Sudhakar, S., & Prakash, K. B., 2020. Effective handwritten digit recognition using deep convolution neural network. International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 1335-1339.
Vapnik Vladimir, N., 1995. The nature of statistical learning theory.
Gope, B., Pande, S., Karale, N., Dharmale, S., & Umekar, P., 2021. Handwritten digits identification using MNIST database via machine learning models. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012108). IOP Publishing.
Ahlawat, S., & Choudhary, A., 2020. Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167, 2554-2560.
Pashine, S., Dixit, R., & Kushwah, R., 2021. Handwritten Digit Recognition using Machine and Deep Learning Algorithms. arXiv preprint arXiv:2106.12614.
Grover, D., & Toghi, B., 2019. MNIST dataset classification utilizing k-NN classifier with modified sliding-window metric. In Science and Information Conference (pp. 583-591). Springer, Cham.