SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (2203.10209v1)

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.

Analysis of SwinTextSpotter: Enhancing Scene Text Spotting through Synergistic Text Detection and Recognition

The paper "SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition" introduces a novel end-to-end framework for scene text spotting that seeks to improve the synergy between text detection and text recognition. This framework, named SwinTextSpotter, emerges from the observation that current state-of-the-art methods often fail to optimally integrate these two tasks, typically only sharing features through a unified backbone. The key innovation in this work is the Recognition Conversion mechanism, which facilitates explicit interactions between detection and recognition processes and helps achieve superior performances across several challenging datasets.

Methodology and Design Principles

SwinTextSpotter leverages recent advancements in transformer-based architectures and incorporates them into the design through a Swin-Transformer-based backbone. This choice highlights the framework's focus on capturing both local and global contexts essential for accurately identifying text in images with complex backgrounds. The Dilated Swin-Transformer extends this capability by incorporating dilated convolutions, thus enhancing receptive fields suitable for capturing long-range dependencies, which is crucial for distinguishing between closely spaced text regions.

The core contribution of the system is the Recognition Conversion (RC) module, designed to bridge detection and recognition more effectively. By incorporating features from the detection head into the recognition stage through this module, SwinTextSpotter enables a feedback loop where recognition losses can back-propagate to influence detection and localization tasks. This architectural synergy is claimed to address common issues such as background noise interference and suboptimal text boundary handling.

Quantitative Results and Performance Benchmarking

The performance analysis spans various datasets, including multi-oriented (ICDAR 2015, RoIC13) and arbitrarily-shaped text datasets (Total-Text, CTW1500), as well as multilingual datasets (ReCTS, VinText). SwinTextSpotter shows remarkable improvements, achieving notable end-to-end F-measure gains on the Total-Text and CTW1500 datasets relative to existing methods such as ABCNet v2 and Mask TextSpotter v3. Specifically, the framework displays a profound ability to detect and recognize texts in multi-lingual settings, showcasing a 9.8% increase in 1-NED on ReCTS compared to ABCNet v2. Furthermore, the model surpasses prior limits in rotating text detection as observed in RoIC13 experimental benchmarks.

Implications and Future Directions

Practically, the proposed framework streamlines the pipeline for text spotting by eliminating the need for character-level annotations and explicit rectification modules. This not only simplifies the integration process but also facilitates maintenance and adaptation for diverse textual inputs. Theoretically, SwinTextSpotter's robust design principles may inspire further research into synergistic multi-task learning approaches within computer vision applications, particularly where feature overlaps can be exploited for mutual task enhancement.

Future developments in AI and scene text recognition could explore optimizing transformer architectures or investigate lightweight versions to address computational efficiency further, especially for real-time applications like autonomous navigation and augmented reality. Additionally, extending the interaction mechanism to integrate linguistic models that could further inform both detection and recognition based on contextual information might yield even richer performance enhancements.

Conclusion

SwinTextSpotter presents a well-supported argument for a more integrated approach to scene text recognition and detection. The paper successfully illustrates how synergistic task design can lead to substantial improvements over traditional architectures relying predominantly on shared backbones. By adopting this cohesive framework, SwinTextSpotter sets a new standard for integrating detection and recognition tasks within scene text spotting, exemplifying potential directions in deep learning and computer vision domains.