Abstract
The study explores the integration of transfer learning (TL) with mobile-enabled convolutional neural networks (MbNets) to enhance Arabic Handwritten Character Recognition (AHCR). Addressing challenges like extensive computational requirements and dataset scarcity, this research evaluates three TL strategies--full fine-tuning, partial fine-tuning, and training from scratch--using four lightweight MbNets: MobileNet, SqueezeNet, MnasNet, and ShuffleNet. Experiments were conducted on three benchmark datasets: AHCD, HIJJA, and IFHCDB. MobileNet emerged as the top-performing model, consistently achieving superior accuracy, robustness, and efficiency, with ShuffleNet excelling in generalization, particularly under full fine-tuning. The IFHCDB dataset yielded the highest results, with 99% accuracy using MnasNet under full fine-tuning, highlighting its suitability for robust character recognition. The AHCD dataset achieved competitive accuracy (97%) with ShuffleNet, while HIJJA posed significant challenges due to its variability, achieving a peak accuracy of 92% with ShuffleNet. Notably, full fine-tuning demonstrated the best overall performance, balancing accuracy and convergence speed, while partial fine-tuning underperformed across metrics. These findings underscore the potential of combining TL and MbNets for resource-efficient AHCR, paving the way for further optimizations and broader applications. Future work will explore architectural modifications, in-depth dataset feature analysis, data augmentation, and advanced sensitivity analysis to enhance model robustness and generalizability.
Abstract (translated)
这项研究探讨了将迁移学习（TL）与移动设备支持的卷积神经网络（MbNets）结合，以增强阿拉伯手写字符识别（AHCR）。为了解决诸如计算需求量大和数据集稀缺等挑战，本研究评估了三种迁移学习策略——完全微调、部分微调以及从头开始训练，使用四种轻量级的MbNet模型：MobileNet、SqueezeNet、MnasNet 和 ShuffleNet。实验在三个基准数据集上进行：AHCD、HIJJA 和 IFHCDB。 MobileNet 凸显为表现最佳的模型，在精度、鲁棒性和效率方面持续表现出色，而ShuffleNet 在完全微调的情况下，尤其在泛化性能方面表现出色。IFHCDB 数据集取得了最高结果，使用 MnasNet 进行完全微调达到了99% 的准确率，突显其对于字符识别的稳健性。AHCD 数据集利用 ShuffleNet 达到了有竞争力的 97% 准确率，而 HIJJA 数据集由于其变异性带来了重大挑战，在使用 ShuffleNet 的情况下达到最高的 92% 准确率。 值得注意的是，完全微调策略展示了最佳的整体性能，很好地平衡了精度和收敛速度，相比之下部分微调在各项指标中表现较差。这些发现强调了将迁移学习与MbNets 结合应用于资源高效的AHCR的潜力，并为进一步优化及更广泛的应用铺平道路。未来的研究工作将进一步探索架构改进、深入的数据集特征分析、数据增强以及高级敏感性分析，以提高模型的鲁棒性和泛化能力。
URL
https://arxiv.org/abs/2509.05019
