轻量级卷积网络集成，高效识别阿拉伯手写字符
Embedded ConvNet Ensembles: A Lightweight Approach to Recognize Arabic Handwritten Characters
- 将轻量嵌入式卷积网络与集成学习结合，降低计算开销
- 在资源受限设备上实现媲美甚至超越重型模型的准确率
- 软投票集成策略表现最佳，适合边缘部署场景
阿拉伯手写字符识别（AHCR）近年来借助深度卷积神经网络（ConvNets）取得显著进展。然而，文献中的许多模型结构深、参数量和浮点运算量（FLOPs）高，限制了其在资源受限设备上的部署，而这类设备日益普及。本研究通过结合轻量级嵌入式卷积网络与集成学习技术，填补这一空白。通过大量实验，系统评估了训练超参数、学习策略、模型选择及集成方法对AHCR的影响。结果表明，嵌入式模型可达到与更重架构相当甚至更高的准确率。集成学习在仅增加少量计算开销的情况下进一步提升性能，尤其在挑战性训练条件下表现突出。其中，软投票策略获得最佳综合效果。
原文摘要 · Abstract (English)
Arabic Handwritten Character Recognition (AHCR) has recently advanced significantly with deep Convolutional Neural Networks (ConvNets). However, many models in the literature are deep and computationally expensive in terms of parameters and FLOPs, limiting their deployment on resource-constrained devices, which are increasingly common. This study addresses this gap by proposing a combination of lightweight embedded ConvNet models and ensemble learning techniques. Extensive experiments were conducted to identify best practices in AHCR, considering training hyperparameters, learning strategies, model choices, and ensemble methods. Results show that embedded models can achieve accuracy comparable to, or even surpassing, heavier architectures. Ensemble learning further enhances performance with only modest computational overhead, particularly under challenging training scenarios. Among the ensembling strategies, soft voting yielded the best overall results.
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