Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
Mingkun Yang Biao Yang Minghui Liao Yingying Zhu Xiang Bai
Scene text recognition is a rapidly developing field that faces numerous
challenges due to the complexity and diversity of scene text, including complex
backgrounds, diverse fonts, flexible arrangements, and accidental occlusions.
In this paper, we propose a novel approach called Class-Aware Mask-guided
feature refinement (CAM) to address these challenges. Our approach introduces
canonical class-aware glyph masks generated from a standard font to effectively
suppress background and text style noise, thereby enhancing feature
discrimination. Additionally, we design a feature alignment and fusion module
to incorporate the canonical mask guidance for further feature refinement for
text recognition. By enhancing the alignment between the canonical mask feature
and the text feature, the module ensures more effective fusion, ultimately
leading to improved recognition performance. We first evaluate CAM on six
standard text recognition benchmarks to demonstrate its effectiveness.
Furthermore, CAM exhibits superiority over the state-of-the-art method by an
average performance gain of 4.1% across six more challenging datasets, despite
utilizing a smaller model size. Our study highlights the importance of
incorporating canonical mask guidance and aligned feature refinement techniques
for robust scene text recognition. The code is available at
https://github.com/MelosY/CAM.