Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition (2103.06495v1)

Abstract: Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of LLMs comes from: 1) implicitly LLMing; 2) unidirectional feature representation; and 3) LLM with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet for scene text recognition. Firstly, the autonomous suggests to block gradient flow between vision and LLMs to enforce explicitly LLMing. Secondly, a novel bidirectional cloze network (BCN) as the LLM is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for LLM which can effectively alleviate the impact of noise input. Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively. Extensive experiments indicate that ABINet has superiority on low-quality images and achieves state-of-the-art results on several mainstream benchmarks. Besides, the ABINet trained with ensemble self-training shows promising improvement in realizing human-level recognition. Code is available at https://github.com/FangShancheng/ABINet.

Autonomous, Bidirectional, and Iterative LLMing for Scene Text Recognition

The paper "Read Like Humans: Autonomous, Bidirectional and Iterative LLMing for Scene Text Recognition" addresses the challenge of effectively incorporating linguistic knowledge into scene text recognition networks. Recognizing text in diverse and challenging environments, such as occluded or low-quality scenes, is a pressing issue in computer vision. This paper proposes a novel framework, ABINet, that adopts an autonomous, bidirectional, and iterative (ABI) approach to improve scene text recognition by explicitly modeling linguistic rules.

Key Contributions

1. Autonomous Modeling: The authors identify the limitations of implicit LLMing and propose separating vision and LLMs. By blocking gradient flow between vision and language components, the ABINet enforces direct learning of linguistic patterns. This decoupling also allows for pre-training from large-scale datasets independently, enhancing LLM capabilities without vision bias.
2. Bidirectional Representation: A novel bidirectional cloze network (BCN) is utilized as the LLM. Unlike traditional unidirectional models that may miss contextual information, BCN leverages a holistic view to improve feature abstraction. This design ensures richer inference by conditioning predictions on both preceding and succeeding contexts within a text sequence.
3. Iterative Correction: To address noise issues in predictions, ABINet employs an iterative correction mechanism. The framework iteratively refines predictions, effectively reducing the impact of incorrect initial inputs. This progressive refinement is crucial for scenarios with high degrees of visual ambiguity.

The ABINet's adoption of these principles leads to a model that mirrors human-like reading capabilities, leveraging both visual and linguistic cues for improved accuracy.

Experimental Validation

Extensive experimentation demonstrates the effectiveness of ABINet across multiple datasets, including challenging benchmarks like IC15, SVTP, and CUTE80. The model consistently achieves state-of-the-art performance, particularly excelling in conditions with low-quality inputs. Notably, the ABINet trained with ensemble self-training from unlabeled data showed significant performance gains, highlighting the potential of semi-supervised learning approaches.

The paper presents detailed ablation studies that underscore the superiority of each novel component, such as the bidirectional cloze network and iterative strategy. For example, the autonomous approach outperforms traditional models by allowing independent and explicit linguistic learning, while the bidirectional representation captures additional context, enhancing recognition accuracy.

Implications and Future Directions

The significant performance improvements suggest that ABINet's framework could be effectively applied to other sequential prediction tasks where context plays a vital role. Additionally, the potential of pre-training LLMs on large unlabeled datasets presents a lucrative direction for enhancing linguistic models in computer vision applications.

Looking forward, this research may also inspire further investigations into the integration of more sophisticated linguistic models and the exploration of unsupervised learning paradigms. By leveraging large, unlabeled datasets, future work could push the boundaries of what is achievable in scene text recognition, paving the way for new applications and technologies.

Overall, ABINet provides a robust foundation for future advancements in scene text recognition by aligning machine reading strategies closely with human capabilities.