DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting (2305.19957v2)

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. Besides, they overlook the exploring on multilingual text spotting which requires an extra script identification task. In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single decoder with explicit points solo for text detection, recognition, and script identification 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. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, our method not only performs well in English scenes but also masters the transcription with complex font structure and a thousand-level character classes, such as Chinese. On the other hand, our DeepSolo++ achieves better performance on the additionally introduced script identification task with a simpler training pipeline compared with previous methods. In addition, our models are also compatible with line annotations, which require much less annotation cost than polygons. The code is available at \url{https://github.com/ViTAE-Transformer/DeepSolo}.

Overview of DeepSolo++: An Efficient Solution for Multilingual Text Spotting

The paper "DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting" introduces a novel approach aimed at streamlining and enhancing the process of multilingual text spotting. The paper tackles the complex task of integrating text detection, recognition, and script identification into a unified framework, leveraging a simplified model architecture that draws inspiration from the DETR paradigm. DeepSolo++, the proposed model, focuses on achieving high performance in multilingual environments with a single, straightforward Transformer-based architecture.

Key Contributions

1. Explicit Point Query Design: The authors introduce an explicit point query representation derived from Bezier center curves. This novel query form is used to encode position, shape, and semantics of text instances in a concise manner, which facilitates the integration of detection, recognition, and script identification tasks through a single decoder framework.
2. Simplification of the Text Spotting Pipeline: DeepSolo++ eliminates the need for heuristic post-processing steps typical in previous architectures, like RoI-based feature extraction and complex language prediction networks. This reduction in architectural complexity results in improved training efficiency and robustness, particularly in scenarios with weak annotations.
3. Comprehensive Multilingual Capability: The model demonstrates strong extensibility in handling various text scripts through a multilingual routing mechanism, leveraging a simple script token to facilitate script identification and appropriate routing for character classification. The paper validates the model's effectiveness on multiple challenging datasets, particularly highlighting its ability to handle diverse character classes and complex script structures like Chinese.
4. Strong Performance Metrics: The experimental results are impressive, with DeepSolo++ achieving state-of-the-art results across various monolingual and multilingual benchmarks. Notably, on the ICDAR 2019 MLT dataset, the model demonstrates significant performance improvements in joint detection and script identification tasks (5.5% H-mean and 8.0% AP improvements), and robust recognition capabilities in end-to-end text spotting scenarios (2.7% H-mean gain).

Practical and Theoretical Implications

The practical implications of DeepSolo++ are profound, as the model provides a flexible, cost-effective solution for real-world applications requiring effective multilingual text recognition and identification. The model's efficiency and simplicity potentially lower the barrier for deploying sophisticated text spotting systems in various applications, such as intelligent navigation and multilingual information retrieval systems.

From a theoretical perspective, this research contributes to the understanding of leveraging Transformer architectures for complex object detection tasks by showcasing the efficacy of point-based representations in simplifying and enhancing performance. The insights gained from this work could guide future developments in Transformer models applied to other domains requiring integrated detection and recognition tasks.

Speculative Future Directions

Future developments inspired by this research could involve further exploration of the synergy between text representation, detection, and recognition architectures. Possible areas of focus include refining the Transformer architecture for better long-tail recognition, integrating more powerful LLMs to enhance recognition accuracy, and tailoring the encoder-decoder mechanism for dynamic adaptation to varying text scripts and structures.

Additionally, investigating solutions to the current challenges in inverse-like text detection and recognition could result in a more robust text spotting framework capable of handling even more diverse real-world scenarios. The paper provides a foundational step towards realizing a comprehensive, efficient, and versatile text spotting solution fit for a multitude of languages and scripts.