Handwritten Text Recognition (HTR) is a relevant problem in computer vision,
and implies unique challenges owing to its inherent variability and the rich
contextualization required for its interpretation. Despite the success of
Self-Supervised Learning (SSL) in computer vision, its application to HTR has
been rather scattered, leaving key SSL methodologies unexplored. This work
focuses on one of them, namely Spatial Context-based SSL. We investigate how
this family of approaches can be adapted and optimized for HTR and propose new
workflows that leverage the unique features of handwritten text. Our
experiments demonstrate that the methods considered lead to advancements in the
state-of-the-art of SSL for HTR in a number of benchmark cases.

手写文本识别 (HTR) 是计算机视觉中一个相关的问题，由于其固有的可变性和对其解释所需的丰富的环境依赖性，其面临独特的挑战。本文研究了一种名为空间上下文自我监督学习（Spatial Context-based SSL）的方法，并探索了该方法在 HTR 中的应用和优化。我们的实验表明，所考虑的方法在一些基准案例中推动了 HTR 领域自我监督学习的最新技术进展。