Scene Text Recognition with Permuted Autoregressive Sequence Models (2207.06966v1)

Abstract: Context-aware STR methods typically use internal autoregressive (AR) LLMs (LM). Inherent limitations of AR models motivated two-stage methods which employ an external LM. The conditional independence of the external LM on the input image may cause it to erroneously rectify correct predictions, leading to significant inefficiencies. Our method, PARSeq, learns an ensemble of internal AR LMs with shared weights using Permutation LLMing. It unifies context-free non-AR and context-aware AR inference, and iterative refinement using bidirectional context. Using synthetic training data, PARSeq achieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and more challenging datasets. It establishes new SOTA results (96.0% accuracy) when trained on real data. PARSeq is optimal on accuracy vs parameter count, FLOPS, and latency because of its simple, unified structure and parallel token processing. Due to its extensive use of attention, it is robust on arbitrarily-oriented text which is common in real-world images. Code, pretrained weights, and data are available at: https://github.com/baudm/parseq.

• The paper introduces a unified PARSeq model that merges context-free and context-aware inference for streamlined scene text recognition.
• The paper employs permutation language modeling to enable flexible, iterative decoding that robustly handles arbitrarily-oriented text.
• The paper achieves state-of-the-art performance with 91.9% accuracy on synthetic and 96.0% on real-world datasets, enhancing efficiency and practical applicability.

Scene Text Recognition with Permuted Autoregressive Sequence Models: A Review

The paper "Scene Text Recognition with Permuted Autoregressive Sequence Models" introduces an innovative approach to Scene Text Recognition (STR) leveraging Permuted Autoregressive Sequence (PARSeq) models. This research addresses key limitations in existing STR methodologies, particularly the integration of context-aware LLMs within STR frameworks, aiming to refine performance in recognizing complex scene text.

Overview of Techniques and Methodologies

The primary contribution of this work is the introduction of PARSeq, a model that improves upon traditional autoregressive (AR) architectures by employing Permutation LLMing (PLM). This enables the learning of an ensemble of AR models with shared weights. Unlike two-stage methods requiring an external LLM, PARSeq unifies context-free non-AR and context-aware AR inference, thereby eliminating inefficiencies associated with conditional independence of predictions on external models.

Key features of PARSeq include:

• Unified Architecture: PARSeq seamlessly combines context-free and context-aware capabilities within a single model structure, optimizing both accuracy and computational efficiency.
• Permutation LLMing: Trained using PLM, PARSeq benefits from multiple permuted sequences, allowing flexible decoding and robust iterative refinement.
• Robustness to Arbitrary Orientations: Extensive use of attention mechanisms enhances the model's ability to process arbitrarily-oriented text, a common challenge in real-world scenes.

Numerical Results

The paper reports that PARSeq achieves state-of-the-art results, attaining 91.9% accuracy on STR benchmarks using synthetic data and establishing 96.0% accuracy when trained on real-world datasets. These results underscore PARSeq's ability to balance accuracy, parameter count, FLOPS, and latency, making it a compelling choice for applications requiring efficient text recognition.

Implications and Future Directions

PARSeq's contributions lie in its model simplicity and unified approach to handling different types of context in STR tasks. The implications of this research extend to various domains where text recognition from images is crucial, such as autonomous vehicles, augmented reality, and assistive technologies for the visually impaired. By minimizing reliance on external models and enhancing robustness across varied text orientations and conditions, PARSeq sets a new precedent in STR efficiency and applicability.

Future work could explore the broader integration of PARSeq in other sequence modeling tasks beyond STR, given its generalizable framework. Additionally, experiments on larger, more diverse datasets will further validate the robustness of PARSeq in real-world applications. The exploration of PLM in other domains of NLP where context, sequence, and orientation play critical roles could also be a promising avenue for research.

Conclusion

In summary, this paper presents a significant step forward in STR methodologies by introducing PARSeq, a model that not only achieves high accuracy but also demonstrates efficiency and adaptability across various scene complexities. The use of PLM and attention mechanisms proves beneficial in overcoming traditional AR model limitations, paving the way for more versatile and powerful text recognition systems.