Masked Vision-Language Transformers for Scene Text Recognition

• URL: http://arxiv.org/abs/2211.04785v1
• Date: Wed, 9 Nov 2022 10:28:23 GMT
• Title: Masked Vision-Language Transformers for Scene Text Recognition
• Authors: Jie Wu, Ying Peng, Shengming Zhang, Weigang Qi, Jian Zhang
• Abstract summary: Scene text recognition (STR) enables computers to recognize and read the text in various real-world scenes. Recent STR models benefit from taking linguistic information in addition to visual cues into consideration. We propose a novel Masked Vision-Language Transformers (MVLT) to capture both the explicit and the implicit linguistic information.
• Score: 10.057137581956363
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scene text recognition (STR) enables computers to recognize and read the text in various real-world scenes. Recent STR models benefit from taking linguistic information in addition to visual cues into consideration. We propose a novel Masked Vision-Language Transformers (MVLT) to capture both the explicit and the implicit linguistic information. Our encoder is a Vision Transformer, and our decoder is a multi-modal Transformer. MVLT is trained in two stages: in the first stage, we design a STR-tailored pretraining method based on a masking strategy; in the second stage, we fine-tune our model and adopt an iterative correction method to improve the performance. MVLT attains superior results compared to state-of-the-art STR models on several benchmarks. Our code and model are available at https://github.com/onealwj/MVLT.

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