LEGO: Self-Supervised Representation Learning for Scene Text Images
This addresses the scarcity of annotated real-world data for scene text recognition, enabling better performance in real-world applications, though it is incremental as it builds on existing self-supervised approaches.
The paper tackles the problem of distribution gap between synthetic and real data in scene text recognition by proposing LEGO, a self-supervised representation learning method that uses three novel pre-text tasks to model sequential, semantic, and structural features, achieving superior or comparable performance to state-of-the-art methods on six benchmarks.
In recent years, significant progress has been made in scene text recognition by data-driven methods. However, due to the scarcity of annotated real-world data, the training of these methods predominantly relies on synthetic data. The distribution gap between synthetic and real data constrains the further performance improvement of these methods in real-world applications. To tackle this problem, a highly promising approach is to utilize massive amounts of unlabeled real data for self-supervised training, which has been widely proven effective in many NLP and CV tasks. Nevertheless, generic self-supervised methods are unsuitable for scene text images due to their sequential nature. To address this issue, we propose a Local Explicit and Global Order-aware self-supervised representation learning method (LEGO) that accounts for the characteristics of scene text images. Inspired by the human cognitive process of learning words, which involves spelling, reading, and writing, we propose three novel pre-text tasks for LEGO to model sequential, semantic, and structural features, respectively. The entire pre-training process is optimized by using a consistent Text Knowledge Codebook. Extensive experiments validate that LEGO outperforms previous scene text self-supervised methods. The recognizer incorporated with our pre-trained model achieves superior or comparable performance compared to state-of-the-art scene text recognition methods on six benchmarks. Furthermore, we demonstrate that LEGO can achieve superior performance in other text-related tasks.