让手写文档识别模型在测试时自我适应，提升复杂场景下的识别准确率。
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning
- 测试时用自监督学习动态调整模型参数，实现输入自适应。
- 在多个基准数据集上超越现有最佳方法，显著提升识别精度。
- 适合低标注数据、手写风格多样的实际文档识别场景。
尽管手写文档识别（HDR）近年来取得显著进展，但在复杂背景、多样手写风格和多变文档布局下实现高效准确的文本识别仍是实际挑战，且学术研究对此关注较少，尤其在标注数据极少的情况下。本文提出DocTTT框架解决此问题。核心创新在于利用测试时训练（Test-Time Training）在测试阶段对每个输入进行模型自适应。我们提出一种新型元辅助学习方法，结合元学习与自监督掩码自动编码器（MAE）。测试时，通过自监督MAE损失调整视觉表征参数；训练时，采用元学习框架优化模型参数，使其能有效适应新输入。实验结果表明，该方法在多个基准数据集上显著优于现有最先进方法。
原文摘要 · Abstract (English)
Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder~(MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets.
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