Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available.

翻译：自监督学习近来在文档分析领域展现出强大潜力。这些方法能够学习高质量图像表征，并克服需要大量标注数据的监督方法的局限性。然而，现有方法无法以增量方式捕捉新知识——即数据按顺序呈现给模型的情景，而这更接近真实场景。本文以序列识别任务中的手写文本识别为例，探索持续自监督学习在缓解灾难性遗忘问题上的潜力。我们的方法通过为每个任务添加称为适配器的中间层，并在学习当前任务时高效蒸馏先前模型的知识。所提出的框架在计算和内存复杂度上均具有高效性。为证明其有效性，我们将学习到的模型迁移至涵盖拉丁文与非拉丁文的多项文本识别下游任务进行评估。据我们所知，这是持续自监督学习在手写文本识别领域的首次应用。我们在英语、意大利语和俄语文本上取得了最先进性能，同时每个任务仅增加少量参数。代码与训练模型将公开提供。