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
this https URL

提出了一种名为 Class-Aware Mask-guided feature refinement（CAM）的新方法来改善复杂背景、文本样式噪声等方面对场景文本识别的挑战，并通过标准字体生成规范的类感知字形掩码来增强特征区分性、设计特征对齐和融合模块来进一步提升文本识别的特征细化。在六个标准文本识别基准测试中，CAM 展示出优于先进方法的优势，平均性能提高了 4.1％，并且使用较小的模型尺寸。该研究强调了在鲁棒场景文本识别中融入规范的掩码指导和对齐特征细化技术的重要性。