Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution (2112.08171v1)

Abstract: In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.

Analysis of "Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution"

The paper "Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution" introduces a novel approach to enhancing low-resolution scene text images by incorporating stroke-level details. This research extends traditional super-resolution methods by leveraging insights from Gestalt Psychology, emphasizing the importance of stroke details in text images.

Core Contribution

The authors propose a Stroke-Aware Scene Text Image Super-Resolution method that focuses on enhancing the visual quality of stroke details, using a Stroke-Focused Module (SFM). Unlike conventional methods that treat text images as general images, the SFM emphasizes stroke-level internal structures, providing finer granularity by decomposing characters and digits at the stroke level.

Methodology

The proposed methodology consists of two main components:

1. Pixel-wise Supervision Module (PSM): This module is based on traditional CNN-based architectures and is responsible for color and contour recovery in low-resolution images. It uses standard L2 loss to supervise the super-resolution task.
2. Stroke-Focused Module (SFM): The SFM directs attention to stroke regions, guiding the recovery process with stroke-level attention maps generated by an auxiliary recognizer pre-trained on synthetic datasets. The use of stroke-level attention maps, rather than character-level, provides more fine-grained guidance.

Experimental Validation

The research demonstrates the efficacy of the proposed approach through experiments on TextZoom and a specially constructed Chinese character dataset, Degraded-IC13. The results highlight significant improvements in recognition accuracy, underscoring the model’s capability to produce more distinguishable images. The paper reports a notable enhancement of up to 7.5% in recognition accuracy on challenging subsets of TextZoom.

Theoretical and Practical Implications

Theoretically, this research emphasizes the importance of employing fine-grained units such as strokes in image reconstruction tasks, challenging the commonplace reliance on character-level approaches. Practically, this method provides a robust preprocessing step for applications in environments where low-resolution text images are prevalent.

Future Directions

Future research could explore extending the stroke-level approach to other character-based languages and evaluating efficacy in more diverse scenes. Additionally, integrating this approach with semantically-aware models could yield further improvements in contexts requiring linguistic context understanding.

Conclusion

This paper effectively addresses the persistent challenge of low-resolution text image recognition by deploying a stroke-aware approach. The utilization of Gestalt principles to focus on stroke-level detail represents a meaningful advancement in the field of scene text recognition. The insights provided here pave the way for more nuanced and effective super-resolution techniques in AI.