HANDWRITTEN CHARACTER RECOGNITION IN ASSYRIAN LANGUAGE USING CONVOLUTIONAL NEURAL NETWORK
DOI:
https://doi.org/10.25271/sjuoz.2024.12.1.1189Keywords:
Deep Learning, Convolutional Neural Network, Handwritten Character Recognition, Assyrian LanguageAbstract
Academics and researchers worldwide have paid close attention to biometric handwriting recognition using deep learning as much research has been proposed to enhance biometric recognition in the past and in recent years. Several solutions for character recognition systems in various languages, including Chinese, English, Japanese, Arabic, and Kurdish have been developed. Unfortunately, there has been minimal growth in the Assyrian language. There is still little research on Assyrian handwriting. In this paper, a new Assyrian language dataset was created as part of the procedure by distributing 500 forms consisting of 36 Assyrian characters to people between the ages of 13 and 60 of both genders. The preprocessing operation includes cleaning the noisy data and segmenting each image to 224x224 pixels. This effort resulted in the collection of 18,000 images of these characters to be trained 70% and tested 30% in four CNN models, VGG16, VGG19, MobileNet-V2, and ResNet-50, over 30 epochs to give an accuracy rate of 90.97%, 92.06%, 95.70%, and 94.97%., respectively.
References
Adebayo, S., Oluwatobi Aworinde, H., Akinwunmi, A. O., Ayandiji, A., & Olalekan Monsir, A. (2022). Convolutional neural network-based crop disease detection model using transfer learning approach. Indonesian Journal of Electrical Engineering and Computer Science, 29(1), 365. doi:10.11591/ijeecs.v29.i1.pp365-374
Ahlawat, S., & Choudhary, A. (2020). Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167, 2554-2560.
Albahli, S., Nawaz, M., Javed, A., & Irtaza, A. (2021). An improved faster-RCNN model for handwritten character recognition. Arabian Journal for Science and Engineering, 46(9), 8509-8523.
Ali, A. A. A., & Mallaiah, S. (2022). Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout. Journal of King Saud University-Computer and Information Sciences, 34(6), 3294-3300.
Almisreb, A. A., Turaev, S., Saleh, M. A., & Al Junid, S. A. M. (2022). Arabic Handwriting Classification using Deep Transfer Learning Techniques. Pertanika Journal of Science & Technology, 30(1).
Altwaijry, N., & Al-Turaiki, I. (2021). Arabic handwriting recognition system using convolutional neural network. Neural Computing and Applications, 33(7), 2249-2261.
Ashiquzzaman, A., Tushar, A. K., Rahman, A., & Mohsin, F. (2019). An efficient recognition method for handwritten arabic numerals using CNN with data augmentation and dropout Data management, analytics and innovation (pp. 299-309): Springer.
Balaha, H. M., Ali, H. A., Saraya, M., & Badawy, M. (2021). A new Arabic handwritten character recognition deep learning system (AHCR-DLS). Neural Computing and Applications, 33(11), 6325-6367.
Benjamen, A. (2022). Assyrians in Modern Iraq: Negotiating Political and Cultural Space: Cambridge University Press.
Bhatnagar, S., Gill, L., & Ghosh, B. (2020). Drone image segmentation using machine and deep learning for mapping raised bog vegetation communities. Remote Sensing, 12(16), 2602.
Boutounte, M., & Ouadid, Y. (2021). Characters recognition using keys points and convolutional neural network. Indonesian Journal of Electrical Engineering and Computer Science, 22(3), 1629. doi:10.11591/ijeecs.v22.i3.pp1629-1634
Chandankhede, C., & Sachdeo, R. (2023). Offline MODI script character recognition using deep learning techniques. Multimedia Tools and Applications, 1-12.
Chatterjee, S., Dutta, R. K., Ganguly, D., Chatterjee, K., & Roy, S. (2019). Bengali handwritten character classification using transfer learning on deep convolutional neural network. arXiv preprint arXiv:1902.11133.
Chatterjee, S., Dutta, R. K., Ganguly, D., Chatterjee, K., & Roy, S. (2020). Bengali handwritten character classification using transfer learning on deep convolutional network. Paper presented at the Intelligent Human Computer Interaction: 11th International Conference, IHCI 2019, Allahabad, India, December 12–14, 2019, Proceedings 11.
Chauhan, R., Ghanshala, K. K., & Joshi, R. (2018). Convolutional neural network (CNN) for image detection and recognition. Paper presented at the 2018 first international conference on secure cyber computing and communication (ICSCCC).
Dao, H. (2020). Image classification using convolutional neural networks.
Das, A., & Mohanty, M. N. (2020). Use of deep neural network for optical character recognition Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies (pp. 219-254): IGI Global.
De Ridder, J. J. (2018). Descriptive Grammar of Middle Assyrian: Harrassowitz Verlag.
Dubey, A. K., & Jain, V. (2019). Comparative study of convolution neural network’s relu and leaky-relu activation functions. Paper presented at the Applications of Computing, Automation and Wireless Systems in Electrical Engineering: Proceedings of MARC 2018.
Elleuch, M., Jraba, S., & Kherallah, M. (2021). The Effectiveness of Transfer Learning for Arabic Handwriting Recognition using Deep CNN. Journal of Information Assurance & Security, 16(2).
Elleuch, M., & Kherallah, M. (2020). Off-line Handwritten Arabic text recognition using convolutional DL networks. International Journal of Computer Information Systems and Industrial Management Applications, 12, 104-112.
Fales, F. M. (2021). Neo-Assyrian History of the Akkadian Language (2 vols) (pp. 1347-1395): Brill.
Fales, F. M. (2023). The Assyrian Empire. The Oxford History of the Ancient Near East: Volume IV: the Age of Assyria, 1, 425.
Gholamalinezhad, H., & Khosravi, H. (2020). Pooling methods in deep neural networks, a review. arXiv preprint arXiv:2009.07485.
Ghosh, T., Abedin, M., Chowdhury, S., Tasnim, Z., Karim, T., Reza, S. M. S., . . . Yousuf, M. (2020). Bangla handwritten character recognition using MobileNet V1 architecture. Bulletin of Electrical Engineering and Informatics, 9, 2547-2554. doi:10.11591/eei.v9i6.2234
Halder, M., Kundu, S., & Hasan, M. F. (2023). An Improved Method to Recognize Bengali Handwritten Characters Using CNN, Singapore.
Hamida, S., El Gannour, O., Cherradi, B., Ouajji, H., & Raihani, A. (2022). Handwritten computer science words vocabulary recognition using concatenated convolutional neural networks. Multimedia Tools and Applications, 1-27.
Hossain, M. A., & Ali, M. M. (2019). Recognition of handwritten digit using convolutional neural network (CNN). Global Journal of Computer Science and Technology.
Jayasundara, V., Jayasekara, S., Jayasekara, H., Rajasegaran, J., Seneviratne, S., & Rodrigo, R. (2019). Textcaps: Handwritten character recognition with very small datasets. Paper presented at the 2019 IEEE winter conference on applications of computer vision (WACV).
Jin, G., Liu, Y., Qin, P., Hong, R., Xu, T., & Lu, G. (2023). An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2. Sensors, 23(4), 1953.
Jraba, S., Elleuch, M., & Kherallah, M. (2020). Arabic handwritten recognition system using deep convolutional neural networks. Paper presented at the International Conference on Intelligent Systems Design and Applications.
Jraba, S., Elleuch, M., & Kherallah, M. (2021). Arabic handwritten recognition system using deep convolutional neural networks. Paper presented at the Intelligent Systems Design and Applications: 20th International Conference on Intelligent Systems Design and Applications (ISDA 2020) held December 12-15, 2020.
Kandel, I., & Castelli, M. (2020). Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences, 10(6), 2021.
Khari, M., Garg, A. K., Crespo, R. G., & Verdú, E. (2019). Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks. Int. J. Interact. Multim. Artif. Intell., 5(7), 22-27.
Korichi, A., Slatnia, S., Aiadi, O., Tagougui, N., & Kherallah, M. (2020). Arabic handwriting recognition: Between handcrafted methods and deep learning techniques. Paper presented at the 2020 21st International Arab Conference on Information Technology (ACIT).
Lamsaf, A., Ait Kerroum, M., Boulaknadel, S., & Fakhri, Y. (2022). Recognition of Arabic handwritten words using convolutional neural network. Indonesian Journal of Electrical Engineering and Computer Science, 26(2), 1148. doi:10.11591/ijeecs.v26.i2.pp1148-1155
Lincy, R. B., & Gayathri, R. (2021). Off-Line Tamil Handwritten Character Recognition Based on Convolutional Neural Network with VGG16 and VGG19 Model. Paper presented at the Advances in Automation, Signal Processing, Instrumentation, and Control: Select Proceedings of i-CASIC 2020.
Ma, J., & Yuan, Y. (2019). Dimension reduction of image deep feature using PCA. Journal of Visual Communication and Image Representation, 63, 102578.
Mahmood, M. R., & Abdulrazzaq, M. B. (2022). Performance evaluation of chi-square and relief-F feature selection for facial expression recognition. Indonesian Journal of Electrical Engineering and Computer Science, 27(3), 1470-1478.
Mishra, S., Sachan, R., & Rajpal, D. (2020). Deep convolutional neural network based detection system for real-time corn plant disease recognition. Procedia Computer Science, 167, 2003-2010.
Muhammad, A. R. (2019). The Assyrian language situation in the Kurdistan region of Iraq. Балтийский гуманитарный журнал, 8(1 (26)), 21-23.
Niharmine, L., Outtaj, B., & Azouaoui, A. (2022). Tifinagh handwritten character recognition using optimized convolutional neural network. International Journal of Electrical and Computer Engineering (IJECE), 12, 4164. doi:10.11591/ijece.v12i4.pp4164-4171
Pande, S. D., Jadhav, P. P., Joshi, R., Sawant, A. D., Muddebihalkar, V., Rathod, S., . . . Das, S. (2022). Digitization of handwritten Devanagari text using CNN transfer learning–A better customer service support. Neuroscience Informatics, 2(3), 100016.
Parikh, M., & Desai, A. (2022). Recognition of Handwritten Gujarati Conjuncts Using the Convolutional Neural Network Architectures: AlexNet, GoogLeNet, Inception V3, and ResNet50. Paper presented at the International Conference on Advances in Computing and Data Sciences.
Pragathi, M., Priyadarshini, K., Saveetha, S., Banu, A. S., & Aarif, K. M. (2019). Handwritten tamil character recognition UsingDeep learning. Paper presented at the 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN).
Putri, D. U. K., Pratomo, D. N., & Azhari, A. (2023). Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition. TELKOMNIKA (Telecommunication Computing Electronics and Control), 21(2), 346-353.
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of dimensionality reduction techniques on big data. IEEE Access, 8, 54776-54788.
Sada, E. (2021). Assyrian-Syriac chants from the liturgy of the Church of the East.
Safarzadeh, V. M., & Jafarzadeh, P. (2020). Offline Persian handwriting recognition with CNN and RNN-CTC. Paper presented at the 2020 25th international computer conference, computer society of Iran (CSICC).
Seng, L. M., Chiang, B. B. C., Salam, Z. A. A., Tan, G. Y., & Chai, H. T. (2021). MNIST Handwritten Digit Recognition with Different CNN Architectures. Journal of Applied Technology and Innovation (e-ISSN: 2600-7304), 5(1), 7.
Shams, M., Elsonbaty, A., & ElSawy, W. (2020). Arabic handwritten character recognition based on convolution neural networks and support vector machine. arXiv preprint arXiv:2009.13450.
Siddique, F., Sakib, S., & Siddique, M. A. B. (2019). Recognition of handwritten digit using convolutional neural network in python with tensorflow and comparison of performance for various hidden layers. Paper presented at the 2019 5th international conference on advances in electrical engineering (ICAEE).
Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21(8), 2852.
Truong Quang, V., Duy, L., & Nhan, N. (2020). Vietnamese handwritten character recognition using convolutional neural network. IAES International Journal of Artificial Intelligence (IJ-AI), 9, 276. doi:10.11591/ijai.v9.i2.pp276-281
Vakili, M., Ghamsari, M., & Rezaei, M. (2020). Performance analysis and comparison of machine and deep learning algorithms for IoT data classification. arXiv preprint arXiv:2001.09636.
Valls, J. M., Aler, R., Galván, I. M., & Camacho, D. (2021). Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems. Journal of Ambient Intelligence and Humanized Computing, 12(12), 10515-10527.
Wang, Y., Li, Y., Song, Y., & Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences, 10(5), 1897.
Yang, W., Zhao, Y., Wang, D., Wu, H., Lin, A., & He, L. (2020). Using principal components analysis and IDW interpolation to determine spatial and temporal changes of surface water quality of Xin’anjiang river in Huangshan, China. International journal of environmental research and public health, 17(8), 2942.
Yao, K., & Zheng, Y. (2023). Fundamentals of Machine Learning Nanophotonics and Machine Learning: Concepts, Fundamentals, and Applications (pp. 77-112): Springer.
Yapıcı, M. M., Tekerek, A., & Topaloğlu, N. (2021). Deep learning-based data augmentation method and signature verification system for offline handwritten signature. Pattern Analysis and Applications, 24(1), 165-179.
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