Abstract
Handwritten Text Recognition remains challenging due to the limited data, high writing style variance, and scripts with complex diacritics. Existing approaches, though partially address these issues, often struggle to generalize without massive synthetic data. To address these challenges, we propose HTR-ConvText, a model designed to capture fine-grained, stroke-level local features while preserving global contextual dependencies. In the feature extraction stage, we integrate a residual Convolutional Neural Network backbone with a MobileViT with Positional Encoding block. This enables the model to both capture structural patterns and learn subtle writing details. We then introduce the ConvText encoder, a hybrid architecture combining global context and local features within a hierarchical structure that reduces sequence length for improved efficiency. Additionally, an auxiliary module injects textual context to mitigate the weakness of Connectionist Temporal Classification. Evaluations on IAM, READ2016, LAM and HANDS-VNOnDB demonstrate that our approach achieves improved performance and better generalization compared to existing methods, especially in scenarios with limited training samples and high handwriting diversity.
Abstract (translated)
手写文本识别（Handwritten Text Recognition，HTR）仍然面临挑战，原因在于数据量有限、书写风格变化大以及复杂音调符号的使用。现有的方法尽管部分解决了这些问题，但在没有大量合成数据的情况下往往难以泛化。为应对这些挑战，我们提出了HTR-ConvText模型，该模型旨在捕捉细粒度的笔画级别的局部特征，同时保持全局上下文依赖性。在特征提取阶段，我们将残差卷积神经网络（Residual Convolutional Neural Network）骨干网与带有位置编码块的MobileViT集成在一起，使模型既能捕获结构模式又能学习微妙的书写细节。 接着我们引入了ConvText编码器，这是一种结合全局上下文和局部特征的混合架构，在层次化结构中减少了序列长度以提高效率。此外，一个辅助模块注入文本上下文以减轻连接时序分类（Connectionist Temporal Classification）方法的弱点。 在IAM、READ2016、LAM以及HANDS-VNOnDB数据集上的评估表明，我们的方法相比现有技术实现了更好的性能和泛化能力，尤其是在训练样本有限且手写风格多样化的场景下。
URL
https://arxiv.org/abs/2512.05021
