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Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition
Corresponding Author(s) : Gibran Satya Nugraha
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 2, May 2023
Abstract
The Arabic script is written from right to left and consists of 28 characters, with no capital or lowercase letters. The Arabic script has several orthographic and morphological properties that make handwriting recognition of the Arabic script challenging. In addition, one of the biggest challenges in recognizing Arabic script patterns is the different handwriting styles and characters of each person's writing. The authors propose a study to compare the accuracy of handwriting pattern recognition in Arabic script which has been done previously by comparing five CNN architectures, namely GoogleNet, AlexNet, VGG-16, LeNet-5, and ResNet-50. Considering that previous research has not obtained excellent accuracy. The number of datasets used is 8400 image data and the most optimal comparison of testing and training data is 80:20. Based on the research that has been done, there are several things that the author can conclude. The model is made using 64 filters for each convolution layer because the optimal size is used for 5 architectures, kernel size is 3x3, neurons is 128, dropout weight is 50% to reduce overfitting, learning rate is 0.001, image size is 64x64, the normalization method with the ReLU activation function, and 1-dimensional input image (grayscale), and with a comparison of testing and training data of 80:20. The VGG-16 architectural model is the architecture that gets the highest score, namely 83.99%. This can have good potential to be developed as a medium for learning Arabic script.
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- M. Athoillah and R. K. Putri, “Handwritten arabic numeral character recognition using multi kernel support vector machine,” KINETIK: Game technology, information system, computer network, computing, electronics, and control, pp. 99–106, 2019, Accessed: Jan. 15, 2023. https://doi.org/10.22219/kinetik.v4i2.724
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References
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R. Ahmed et al., “Offline arabic handwriting recognition using deep machine learning: A review of recent advances,” in International conference on brain inspired cognitive systems, Springer, 2020, pp. 457–468. Accessed: Jan. 15, 2023. https://doi.org/10.1007/978-3-030-39431-8_44
S. Dargan, M. Kumar, M. R. Ayyagari, and G. Kumar, “A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning,” Archives of Computational Methods in Engineering, vol. 27, no. 4, pp. 1071–1092, Sep. 2020. https://doi.org/10.1007/s11831-019-09344-w
A. F. Hidayatullah, S. Cahyaningtyas, and R. D. Pamungkas, “Attention-based cnn-bilstm for dialect identification on javanese text,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 317–324, 2020, Accessed: Jan. 15, 2023. https://doi.org/10.22219/kinetik.v5i4.1121
W. Wang and Y. Yang, “Development of convolutional neural network and its application in image classification: a survey,” Optical Engineering, vol. 58, no. 04, p. 1, Apr. 2019. https://doi.org/10.1117/1.OE.58.4.040901
D. Sutaji and H. Rosyid, “Convolutional Neural Network (CNN) Models for Crop Diseases Classification,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 7, no. 2, May 2022. https://doi.org/10.22219/kinetik.v7i2.1443
W. Setiawan, A. Ghofur, F. Hastarita Rachman, and R. Rulaningtyas, “Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 6, no. 4, Nov. 2021. https://doi.org/10.22219/kinetik.v6i4.1335
M. A. Mostafa, M. Al-Qurishi, and H. I. Mathkour, “Towards personality classification through Arabic handwriting analysis,” in The International Research & Innovation Forum, Springer, 2019, pp. 557–565. Accessed: Jan. 15, 2023. https://doi.org/10.1007/978-3-030-30809-4_51
N. Kasim and G. S. Nugraha, “Pengenalan Pola Tulisan Tangan Aksara Arab Menggunakan Metode Convolution Neural Network,” Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ), vol. 3, no. 1, pp. 85–95, 2021, Accessed: Jan. 15, 2023. https://doi.org/10.29303/jtika.v3i1.136
R. Ahmed et al., “Novel deep convolutional neural network-based contextual recognition of Arabic handwritten scripts,” Entropy, vol. 23, no. 3, p. 340, 2021, Accessed: Jan. 15, 2023. https://doi.org/10.3390/e23030340
M. N. AlJarrah, M. Z. Mo’ath, and R. Duwairi, “Arabic handwritten characters recognition using convolutional neural network,” in 2021 12th International Conference on Information and Communication Systems (ICICS), IEEE, 2021, pp. 182–188. Accessed: Jan. 15, 2023. https://doi.org/10.1109/ICICS52457.2021.9464596
B. H. Nayef, S. N. H. S. Abdullah, R. Sulaiman, and Z. A. A. Alyasseri, “Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks,” Multimed Tools Appl, vol. 81, no. 2, pp. 2065–2094, 2022, Accessed: Jan. 15, 2023. https://doi.org/10.1007/s11042-021-11593-6
N. Altwaijry and I. Al-Turaiki, “Arabic handwriting recognition system using convolutional neural network,” Neural Comput Appl, vol. 33, no. 7, pp. 2249–2261, 2021, Accessed: Jan. 14, 2023. https://doi.org/10.1007/s00521-020-05070-8
R. Moumen, R. Chiheb, and R. Faizi, “Real-time Arabic scene text detection using fully convolutional neural networks,” International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 2, p. 1634, Apr. 2021. http://doi.org/10.11591/ijece.v11i2.pp1634-1640
H. Yanmei, W. Bo, and Z. Zhaomin, “An improved LeNet-5 model for Image Recognition,” in Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, New York, NY, USA: ACM, Nov. 2020, pp. 444–448. https://doi.org/10.1145/3443467.3443797
A. Rasheed, N. Ali, B. Zafar, A. Shabbir, M. Sajid, and M. T. Mahmood, “Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet,” IEEE Access, vol. 10, pp. 102629–102645, 2022. https://doi.org/10.1109/ACCESS.2022.3208959
A. A. Almisreb, N. Md Tahir, S. Turaev, M. A. Saleh, and S. A. M. al Junid, “Arabic Handwriting Classification using Deep Transfer Learning Techniques,” Pertanika J Sci Technol, vol. 30, no. 1, pp. 641–654, Jan. 2022. https://doi.org/10.47836/pjst.30.1.35
S. Ibraheem Saleem and A. Mohsin Abdulazeez, “Hybrid Trainable System for Writer Identification of Arabic Handwriting,” Computers, Materials & Continua, vol. 68, no. 3, pp. 3353–3372, 2021. https://doi.org/10.32604/cmc.2021.016342
M. Hacibeyoglu, “Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks,” International Journal of Intelligent Systems and Applications in Engineering, vol. 3, no. 6, pp. 203–208, Sep. 2018. https://doi.org/10.18201/ijisae.2018644778
S. A. Bello, S. Yu, C. Wang, J. M. Adam, and J. Li, “Review: Deep Learning on 3D Point Clouds,” Remote Sens (Basel), vol. 12, no. 11, p. 1729, May 2020. https://doi.org/10.3390/rs12111729
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2016, pp. 770–778. https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.90
Z. Guo, Q. Chen, G. Wu, Y. Xu, R. Shibasaki, and X. Shao, “Village Building Identification Based on Ensemble Convolutional Neural Networks,” Sensors, vol. 17, no. 11, p. 2487, Oct. 2017. https://doi.org/10.3390/s17112487
J. Fan, J. Lee, and Y. Lee, “A Transfer Learning Architecture Based on a Support Vector Machine for Histopathology Image Classification,” Applied Sciences, vol. 11, no. 14, p. 6380, Jul. 2021. https://doi.org/10.3390/app11146380
N. M. Blauch, M. Behrmann, and D. C. Plaut, “Computational insights into human perceptual expertise for familiar and unfamiliar face recognition,” Cognition, vol. 208, p. 104341, Mar. 2021. https://doi.org/10.1016/j.cognition.2020.104341
A. El-Sawy, H. EL-Bakry, and M. Loey, “CNN for Handwritten Arabic Digits Recognition Based on LeNet-5,” 2017, pp. 566–575. https://doi.org/10.1007/978-3-319-48308-5_54