DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting (2211.10772v4)

Abstract: End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. In this paper, we present DeepSolo, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Besides, we also introduce a text-matching criterion to deliver more accurate supervisory signals, thus enabling more efficient training. Quantitative experiments on public benchmarks demonstrate that DeepSolo outperforms previous state-of-the-art methods and achieves better training efficiency. In addition, DeepSolo is also compatible with line annotations, which require much less annotation cost than polygons. The code is available at https://github.com/ViTAE-Transformer/DeepSolo.

Analyzing DeepSolo: A Unified Transformer-Based Approach for Text Spotting

The paper "DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting" introduces an innovative approach to the challenging task of end-to-end text spotting by leveraging a single transformer decoder. The authors propose DeepSolo, a framework designed to efficiently handle both detection and recognition of text in natural scenes through the explicit modeling of character sequences as ordered points, integrating state-of-the-art Transformer techniques.

Key Contributions

1. Unified Framework: DeepSolo employs a single decoder with explicit point queries, representing each text instance as a sequence of ordered points. This novel approach bypasses the intricate coupling required by previous methods that separate detection and recognition tasks, such as RoI-based models. By using Bezier curves to fit scene texts and sample positional queries, DeepSolo effectively captures text semantics and locations concurrently.
2. Improved Training Efficiency: The design achieves notable training efficiency enhancements, attributed to the text matching criterion and the refined representation of scenes through explicit point queries. This criterion leverages script supervision, enhancing the training signals for more accurate detection and recognition.
3. Adaptability and Flexibility: The model's compatibility with line annotations reflects its adaptability, providing a significant reduction in annotation costs compared to polygon annotations. This feature broadens the scope of DeepSolo's applicability across different datasets and annotation types.

Numerical Results and Performance

Quantitative experiments demonstrate that DeepSolo not only exceeds previous state-of-the-art models in terms of accuracy but also achieves superior performance regarding training efficiency. For instance, on benchmarks such as Total-Text and ICDAR 2015, DeepSolo consistently delivers high end-to-end spotting accuracy, particularly when utilizing extensive datasets for pre-training. The integration of datasets like TextOCR further augments the model's capability, exemplifying its scalability.

Practical Implications

Deploying DeepSolo in real-world applications, such as autonomous driving and intelligent navigation, could lead to significant improvements in recognizing and responding to textual information under varying environmental conditions and orientations. The framework's simplification of the text spotting pipeline without a loss in effectiveness presents a highly attractive solution for commercial applications seeking computational efficiency and accuracy without substantial annotation costs.

Theoretical Contributions and Future Directions

From a theoretical standpoint, DeepSolo's approach to representing text through explicit point queries and using a single Transformer decoder challenges traditional multi-module architectures. The insights drawn from such a simplified model could guide future research in deploying Transformers in various computer vision tasks, beyond text spotting.

Moving forward, exploring the adaptation of DeepSolo to multi-language text spotting presents an exciting research avenue, especially in optimizing the explicit query formulation for diverse scripts and languages. Furthermore, integrating robust LLMs can refine recognition accuracy, particularly in cases of complex or challenging text orientations, thereby bridging gaps in current methodologies.

In conclusion, DeepSolo marks a significant advancement in the field of text spotting by proposing a streamlined yet powerful approach. Its capability to bridge the detection and recognition tasks efficiently encompasses promising prospects for both theoretical exploration and practical deployments in various intelligent systems.