Abstract
Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardware improves privacy and lowers latency, but entails memory constraints. To address this, we propose Error-enhanced Contrastive Handwriting Recognition (ECHWR), a training framework designed to improve feature representation and recognition accuracy without increasing inference costs. ECHWR utilizes a temporary auxiliary branch that aligns sensor signals with semantic text embeddings during the training phase. This alignment is maintained through a dual contrastive objective: an in-batch contrastive loss for general modality alignment and a novel error-based contrastive loss that distinguishes between correct signals and synthetic hard negatives. The auxiliary branch is discarded after training, which allows the deployed model to keep its original, efficient architecture. Evaluations on the OnHW-Words500 dataset show that ECHWR significantly outperforms state-of-the-art baselines, reducing character error rates by up to 7.4% on the writer-independent split and 10.4% on the writer-dependent split. Finally, although our ablation studies indicate that solving specific challenges require specific architectural and objective configurations, error-based contrastive loss shows its effectiveness for handling unseen writing styles.
Abstract (translated)
使用惯性测量单元（IMU）进行在线手写识别，使纸质书写成为数字设备的输入方式。在边缘硬件上执行此操作可以提高隐私保护并降低延迟，但会导致内存限制。为了解决这个问题，我们提出了一种名为错误增强对比式手写识别（ECHWR）的训练框架，该框架旨在改进特征表示和识别准确性而不增加推理成本。ECHWR利用一个临时辅助分支，在训练阶段将传感器信号与语义文本嵌入对齐。这种对齐通过双重对比目标来维持：一种是批内对比损失，用于一般模式对齐；另一种则是基于错误的新型对比损失，用于区分正确信号和合成难例（hard negatives）。在完成训练后，辅助分支被丢弃，从而使部署模型能够保持其原始高效架构。在OnHW-Words500数据集上的评估显示，ECHWR显著优于最先进的基线方法，在不依赖书写者的情况下字符错误率降低了高达7.4%，而在依赖书写者的情况下则降低了10.4%。最后，虽然我们的消融研究（ablation studies）表明解决特定挑战需要特定的架构和目标配置，但基于错误的对比损失证明了其处理未见过书写风格的有效性。
URL
https://arxiv.org/abs/2602.07049
