Quantum mechanics

Detector

Linguistics

Voltage

Philosophy

Spotting

Security token

Computer security

Operating system

Telecommunications

Set (abstract data type)

Computer science

Code (set theory)

Natural language processing

Physics

Pattern recognition (psychology)

Image (mathematics)

Feature (linguistics)

Artificial intelligence

Transformer

Text detection

Text recognition

Keyword spotting

Programming language

Feature extraction

Encoder

Annotation

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 (Viet-namese) demonstrate SwinTextSpotter significantly outperforms existing methods. Code is available at https://github.com/mxin262/SwinTextSpotter.

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