Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets

Martin Kišš; Michal Hradiš

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arXiv · 2503.22513

Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets

Martin Kišš·Michal Hradiš

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Abstract

Self-supervised learning has emerged as a powerful approach for leveraging large-scale unlabeled data to improve model performance in various domains. In this paper, we explore masked self-supervised pre-training for text recognition transformers. Specifically, we propose two modifications to the pre-training phase: progressively increasing the masking probability, and modifying the loss function to incorporate both masked and non-masked patches. We conduct extensive experiments using a dataset of 50M unlabeled text lines for pre-training and four differently sized annotated datasets for fine-tuning. Furthermore, we compare our pre-trained models against those trained with transfer learning, demonstrating the effectiveness of the self-supervised pre-training. In particular, pre-training consistently improves the character error rate of models, in some cases up to 30 % relatively. It is also on par with transfer learning but without relying on extra annotated text lines.

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BibTeX RIS

Martin Kišš, Michal Hradiš. 2025-03-28. Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets. https://arxiv.org/abs/2503.22513

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