ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy
  in Transformer
Mingxin Huang Jiaxin Zhang Dezhi Peng Hao Lu Can Huang Yuliang Liu Xiang Bai Lianwen Jin
In recent years, end-to-end scene text spotting approaches are evolving to
the Transformer-based framework. While previous studies have shown the crucial
importance of the intrinsic synergy between text detection and recognition,
recent advances in Transformer-based methods usually adopt an implicit synergy
strategy with shared query, which can not fully realize the potential of these
two interactive tasks. In this paper, we argue that the explicit synergy
considering distinct characteristics of text detection and recognition can
significantly improve the performance text spotting. To this end, we introduce
a new model named Explicit Synergy-based Text Spotting Transformer framework
(ESTextSpotter), which achieves explicit synergy by modeling discriminative and
interactive features for text detection and recognition within a single
decoder. Specifically, we decompose the conventional shared query into
task-aware queries for text polygon and content, respectively. Through the
decoder with the proposed vision-language communication module, the queries
interact with each other in an explicit manner while preserving discriminative
patterns of text detection and recognition, thus improving performance
significantly. Additionally, we propose a task-aware query initialization
scheme to ensure stable training. Experimental results demonstrate that our
model significantly outperforms previous state-of-the-art methods. Code is
available at https://github.com/mxin262/ESTextSpotter.