End-to-end scene text spotting, which aims to read the text in natural
images, has garnered significant attention in recent years. 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 v2, which seeks to find a
better synergy between text detection and recognition. Specifically, we enhance
the relationship between two tasks using novel Recognition Conversion and
Recognition Alignment modules. Recognition Conversion explicitly guides text
localization through recognition loss, while Recognition Alignment dynamically
extracts text features for recognition through the detection predictions. This
simple yet effective design results in a concise framework that requires
neither an additional rectification module nor character-level annotations for
the arbitrarily-shaped text. Furthermore, the parameters of the detector are
greatly reduced without performance degradation by introducing a Box Selection
Schedule. Qualitative and quantitative experiments demonstrate that
SwinTextSpotter v2 achieved state-of-the-art performance on various
multilingual (English, Chinese, and Vietnamese) benchmarks. The code will be
available at
\href{this https URL}{SwinTextSpotter v2}.

SwinTextSpotter v2 是一个新的端到端场景文字识别框架，通过引入 Recognition Conversion 和 Recognition Alignment 模块来加强文本检测和识别之间的关系，从而在各种多语言基准测试中取得了最先进的性能。

SwinTextSpotter v2：场景文字识别的协同优化

SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting

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 this https URL

基于显式协同的文本识别 Transformer 框架 (ESTextSpotter) 在文本检测和识别中，通过建模区分性和交互特征，明显提高了性能。

ESTextSpotter：基于 Transformer 的场景文字定位的显式协同

ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy  in Transformer

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 this https URL

本文提出了一种使用 transformer encoding 的新型端到端场景文本识别框架，并通过一种新的识别转换机制，在不需要额外的矫正模块或字符级别注释的情况下显式地引导文本定位，从而使该方法在多种数据集上显著优于现有方法。

SwinTextSpotter: 基于更好的文本检测与识别协同的场景文本定位

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

Recently end-to-end scene text spotting has become a popular research topic
due to its advantages of global optimization and high maintainability in real
applications. Most methods attempt to develop various region of interest (RoI)
operations to concatenate the detection part and the sequence recognition part
into a two-stage text spotting framework. However, in such framework, the
recognition part is highly sensitive to the detected results (e.g.), the
compactness of text contours). To address this problem, in this paper, we
propose a novel Mask AttentioN Guided One-stage text spotting framework named
MANGO, in which character sequences can be directly recognized without RoI
operation. Concretely, a position-aware mask attention module is developed to
generate attention weights on each text instance and its characters. It allows
different text instances in an image to be allocated on different feature map
channels which are further grouped as a batch of instance features. Finally, a
lightweight sequence decoder is applied to generate the character sequences. It
is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting
and can be trained end-to-end with only coarse position information (e.g.),
rectangular bounding box) and text annotations. Experimental results show that
the proposed method achieves competitive and even new state-of-the-art
performance on both regular and irregular text spotting benchmarks, i.e., ICDAR
2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.

本研究提出了一种名为 MANGO 的新型文本识别模型，其采用遮罩注意力指导的一阶段文本识别框架，可以直接识别无需 ROI 操作的字符序列，并在不同角度、不同排列方式的文本定位任务中取得了新的最优表现。