CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model (2305.14014v4)

Abstract: Pre-trained vision-LLMs~(VLMs) are the de-facto foundation models for various downstream tasks. However, scene text recognition methods still prefer backbones pre-trained on a single modality, namely, the visual modality, despite the potential of VLMs to serve as powerful scene text readers. For example, CLIP can robustly identify regular (horizontal) and irregular (rotated, curved, blurred, or occluded) text in images. With such merits, we transform CLIP into a scene text reader and introduce CLIP4STR, a simple yet effective STR method built upon image and text encoders of CLIP. It has two encoder-decoder branches: a visual branch and a cross-modal branch. The visual branch provides an initial prediction based on the visual feature, and the cross-modal branch refines this prediction by addressing the discrepancy between the visual feature and text semantics. To fully leverage the capabilities of both branches, we design a dual predict-and-refine decoding scheme for inference. We scale CLIP4STR in terms of the model size, pre-training data, and training data, achieving state-of-the-art performance on 13 STR benchmarks. Additionally, a comprehensive empirical study is provided to enhance the understanding of the adaptation of CLIP to STR. Our method establishes a simple yet strong baseline for future STR research with VLMs.

An Expert Overview of "CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-LLM"

The paper "CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-LLM" presents an interesting approach to scene text recognition (STR) by leveraging the capabilities of pre-trained vision-LLMs (VLMs). The authors propose a method called CLIP4STR, which adapts the popular CLIP model for STR tasks, exhibiting significant improvements over existing approaches.

The motivation behind this work stems from observations that traditional STR methods predominantly rely on modality-specific pre-trained models, typically focusing exclusively on the visual modality. However, VLMs like CLIP have shown potential as robust interpreters of both regular and irregular text within images due to their ability to capture rich, cross-modal features. This work capitalizes on CLIP's inherent strengths by transforming it into a scene text reader, effectively creating a baseline STR method with dual encoder-decoder branches: a visual branch and a cross-modal branch.

Key Contributions

1. Dual Encoder-Decoder Branches: CLIP4STR features a dual branch system. The visual branch generates initial predictions using visual features, while the cross-modal branch refines these predictions by addressing discrepancies between visual and text semantics, acting as a semantic-aware spell checker.
2. Predict-and-Refine Inference Scheme: To optimize the utilization of both branches, the authors design a dual predict-and-refine decoding scheme. This inference strategy enhances character recognition by leveraging both modality-specific and cross-modal predictions.
3. Empirical Validation: The paper showcases the effectiveness of CLIP4STR by achieving state-of-the-art results across 11 STR benchmarks, including both regular and irregular texts. The performance improvements underscore the potential of VLMs adapted for STR tasks.
4. Comprehensive Empirical Study: A detailed empirical paper is presented, elucidating the adaptation of CLIP to STR applications. This includes insights into training efficiency and model scaling.

Numerical Results

CLIP4STR's performance is notable, securing top positions in several benchmarks. For instance, accuracy improvements are recorded on challenging datasets like HOST and WOST, with the cross-modal branch significantly enhancing the overall performance. The adaptability and scalability of the model are evident as it outperforms existing methods that employ synthetically trained backbones.

Implications and Future Directions

The implications of this paper extend into practical and theoretical realms, highlighting the potential of leveraging VLMs for STR tasks. This work shifts the paradigm from single-modality pre-training to a more integrated vision-language approach, providing a robust baseline for further exploration in STR research. The use of VLMs like CLIP promises advancements in handling complex visual data, a challenge prevalent in AI applications.

Speculation on Future Developments

Given the success of CLIP4STR, future developments might explore:

• Further refining cross-modal interactions to enhance textual understanding in diverse and dynamic environments.
• Scaling the model architecture while maintaining computational efficiency, thus broadening its applicability across various real-world scenarios.
• Expanding the dataset to incorporate more diverse textual appearances, potentially improving the robustness of STR models.

In conclusion, the paper effectively demonstrates the strength of leveraging vision-LLMs for scene text recognition tasks. CLIP4STR showcases substantial performance enhancements, positioning itself as a formidable baseline for future STR endeavors and indicating substantial room for growth in integrating multi-modal pre-trained models into specialized tasks.