Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition

• URL: http://arxiv.org/abs/2503.18746v1
• Date: Mon, 24 Mar 2025 14:53:35 GMT
• Title: Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
• Authors: Yifei Zhang, Chang Liu, Jin Wei, Xiaomeng Yang, Yu Zhou, Can Ma, Xiangyang Ji,
• Abstract summary: Masked image modeling (MIM) tends to exploit local structures to reconstruct visual patterns, resulting in limited linguistic knowledge.<n>We propose a Linguistics-aware Masked Image Modeling (LMIM) approach, which channels the linguistic information into the decoding process of MIM through a separate branch.
• Score: 50.86415025650168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while the linguistic dimension incorporates contextual and semantic elements. In scenarios with degraded visual quality, linguistic patterns serve as crucial supplements for comprehension, highlighting the necessity of integrating both aspects for robust scene text recognition (STR). Contemporary STR approaches often use language models or semantic reasoning modules to capture linguistic features, typically requiring large-scale annotated datasets. Self-supervised learning, which lacks annotations, presents challenges in disentangling linguistic features related to the global context. Typically, sequence contrastive learning emphasizes the alignment of local features, while masked image modeling (MIM) tends to exploit local structures to reconstruct visual patterns, resulting in limited linguistic knowledge. In this paper, we propose a Linguistics-aware Masked Image Modeling (LMIM) approach, which channels the linguistic information into the decoding process of MIM through a separate branch. Specifically, we design a linguistics alignment module to extract vision-independent features as linguistic guidance using inputs with different visual appearances. As features extend beyond mere visual structures, LMIM must consider the global context to achieve reconstruction. Extensive experiments on various benchmarks quantitatively demonstrate our state-of-the-art performance, and attention visualizations qualitatively show the simultaneous capture of both visual and linguistic information.

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