SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition

Dahua Lin, Zhenghao Peng, Yuliang Liu, Chongyu Liu, Kai Ding, Lianwen Jin, Mingxin Huang, Nicholas Yuan, Shenggao Zhu

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

Repositories linked to this paper

• mxin262/swintextspotter — marked official in Papers with Code; named in the paper
• jacobtyo/swintextspotter
• mxin262/SwinTextSpotter

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Abstract

End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly take advantage of the feature interaction between the two tasks. In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer encoder with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism to explicitly guide text localization through recognition loss. The straightforward design results in a concise framework that requires neither additional rectification module nor character-level annotation for the arbitrarily-shaped text. Qualitative and quantitative experiments on multi-oriented datasets RoIC13 and ICDAR 2015, arbitrarily-shaped datasets Total-Text and CTW1500, and multi-lingual datasets ReCTS (Chinese) and VinText (Vietnamese) demonstrate SwinTextSpotter significantly outperforms existing methods. Code is available at https: //github.com/mxin262/SwinTextSpotter.

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get_code_for_paper("2203.10209")
have("2203.10209")

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