The increasing number of smart devices has led to a rise in the complexity and volume of the image generated. Deep learning is an increasingly common approach for image classification, a fundamental task in many applications. Due to its high computational requirements, implementation in edge devices becomes challenging. Cloud computing serves as an enabler, allowing devices with limited resources to perform deep learning. For cloud computing, however, latency is an issue and is undesirable. Edge computing addresses the issue by redistributing data and tasks closer to the edge. Still, a suitable offloading strategy is required to ensure optimal performance with methods such as LeNet-5, OAHR, and Autoencoder (ANC) as feature extractors paired with different classifiers (such as artificial neural network (ANN) and support vector machine (SVM)). In this study, models are evaluated using a dataset representing Optical Character Recognition (OCR) task. The OCR application has recently been used in many task-offloading studies. The evaluation is based on the time performance and scoring criteria. In terms of time performance, a fully connected ANN using features from the ANC is faster by a factor of over 60 times compared to the fastest performing SVM. Moreover, scoring performance shows that the SVM is less prone to overfit in the case of a noisy or imbalanced dataset in comparison with ANN. So, adopting SVM in which the data distribution is unspecified will be wiser as there is a lower tendency to overfit. The training and inference time, however, are generally higher than ANN.

• Allaire, J. J., & Chollet, F. (2019). keras: R Interface to ‘Keras’. In R package version 2.2.5.0. RStudio. https://CRAN.R-project.org/package=keras.

• Amri, A. A., Ismail, A. R., & Zarir, A. A. (2018). Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data. International Journal of Advanced Computer Science and Applications, 9(2), 258-264. https://doi.org/10.14569/IJACSA.2018.090236

• Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). Training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 144-152). ACM Publishing. https://doi.org/10.1145/130385.130401

• Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121-167. https://doi.org/10.1023/A:1009715923555

• Cao, J., Yang, L., & Cao, J. (2019). Revisiting computation partitioning in future 5G-based edge computing environments. IEEE Internet of Things Journal, 6(2), 2427-2438. https://doi.org/10.1109/JIOT.2018.2869750

• Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. https://doi.org/10.1145/1961189.1961199

• Chang, C., Srirama, S. N., & Buyya, R. (2019). Internet of things (IOT) and new computing paradigms. In Fog and Edge Computing: Principles and Paradigms, 6, 1-23. https://doi.org/10.1002/9781119525080.ch1

• Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674. https://doi.org/10.1109/JPROC.2019.2921977

• Cheriet, M., Kharma, N., Liu, C. L., & Suen, C. Y. (2007). Character recognition systems: A guide for students and practioners. John Wiley & Sons. https://doi.org/10.1002/9780470176535

• Chollet, F. (2016). Building autoencoders in keras. The Keras Blog.

• Ding, C., & Tao, D. (2015). Robust face recognition via multimodal deep face representation. IEEE Transactions on Multimedia, 17(11), 2049-2058. https://doi.org/10.1109/TMM.2015.2477042

• Dube, S., Wan, W. Y., & Nugroho, H. (2021). A novel approach of IoT stream sampling and model update on the IoT edge device for class incremental learning in an edge-cloud system. IEEE Access, 9, 29180-29199. https://doi.org/10.1109/ACCESS.2021.3059251

• Elleuch, M., Lahiani, H., & Kherallah, M. (2016). Recognizing Arabic handwritten script using support vector machine classifier. In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 551-556). IEEE Publishing. https://doi.org/10.1109/ISDA.2015.7489176

• Elleuch, M., Maalej, R., & Kherallah, M. (2016). A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Procedia Computer Science, 80, 1712-1723. https://doi.org/10.1016/j.procs.2016.05.512

• Ghiassirad, H. A., Shoorehdeli, M. A., & Farivar, F. (2019). Application of constrained learning in making deep networks more transparent, regularized, and biologically plausible. Engineering Applications of Artificial Intelligence, 85, 421-428. https://doi.org/10.1016/j.engappai.2019.06.022

• Hammad, M., Liu, Y., & Wang, K. (2019). Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access, 7, 26527-26542. https://doi.org/10.1109/ACCESS.2018.2886573

• Ismail, A., Ahmad, S. A., Soh, A. C., Hassan, M. K., & Harith, H. H. (2020). Deep learning object detector using a combination of convolutional neural network (CNN) architecture (minivggnet) and classic object detection algorithm. Pertanika Journal of Science and Technology, 28(Special Issue 2), 161-171. https://doi.org/10.47836/pjst.28.S2.13

• Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 1-54. https://doi.org/10.1186/s40537-019-0192-5

• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.

• Lecun, Y., Bottou, L., Bengio, Y., & Ha, P. (1998). LeNet. IEEE Publishing.

• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791

• Lee, L. H., Wan, C. H., Rajkumar, R., & Isa, D. (2012). An enhanced support vector machine classification framework by using Euclidean distance function for text document categorization. Applied Intelligence, 37(1), 80-99. https://doi.org/10.1007/s10489-011-0314-z

• Li, B., He, M., Wu, W., Sangaiah, A. K., & Jeon, G. (2018). Computation offloading algorithm for arbitrarily divisible applications in mobile edge computing environments: An OCR case. Sustainability, 10(5), Article 1611. https://doi.org/10.3390/su10051611

• Lin, L., Liao, X., Jin, H., & Li, P. (2019). Computation offloading toward edge computing. Proceedings of the IEEE, 107(8), 1584-1607. https://doi.org/10.1109/JPROC.2019.2922285

• Liu, T., & Gu, Y. (2020). Multiple kernel learning for hyperspectral image classification. Advances in Computer Vision and Pattern Recognition, 55(11), 259-293. https://doi.org/10.1007/978-3-030-38617-7_9

• Nee, S. H., & Nugroho, H. (2020). Task distribution of object detection algorithms in fog-computing framework. In 2020 IEEE Student Conference on Research and Development (SCOReD) (pp. 391-395). IEEE Publishing. https://doi.org/10.1109/SCOReD50371.2020.9251038

• Saeed, S., & Ong, H. C. (2019). Performance of SVM with multiple kernel learning for classification tasks of imbalanced datasets. Pertanika Journal of Science and Technology, 27(1), 527-545.

• Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B., & Liu, D. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61-75. https://doi.org/10.1016/j.knosys.2016.01.002

• Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427-437. https://doi.org/10.1016/j.ipm.2009.03.002

• Sujatha, J., & Rajagopalan, S. P. (2017). Performance evaluation of machine learning algorithms in the classification of parkinson disease using voice attributes. International Journal of Applied Engineering Research, 12(21), 10669-10675.

• Vapnik, V. N. (2000). The nature of statistical learning theory. Springer Science & Business Media. https://doi.org/10.1007/978-1-4757-3264-1

• Varatharajan, R., Manogaran, G., & Priyan, M. K. (2018). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 77(8), 10195-10215. https://doi.org/10.1007/s11042-017-5318-1

• Verma, R., & Ali, J. (2019). Comparative analysis of advanced classification techniques for multilingual ocr systems. International Journal of Scientific and Technology Research, 8(11), 1036-1040.

• Wan, C. H., Lee, L. H., Rajkumar, R., & Isa, D. (2012). A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine. Expert Systems with Applications, 39(15), 11880-11888. https://doi.org/10.1016/j.eswa.2012.02.068

• Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., & Qi, L. (2019). A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Generation Computer Systems, 95, 522-533. https://doi.org/10.1016/j.future.2018.12.055

• Yan, L., Zhang, R., Han, Z., Qin, M., & Yang, S. (2018). Mobile computation offloading strategy based on static information and dynamic partition. In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/VTCSpring.2018.8417705

• Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951