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。

基于类别感知的文本场景识别的掩膜引导特征细化

Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition

Arbitrary text appearance poses a great challenge in scene text recognition
tasks. Existing works mostly handle with the problem in consideration of the
shape distortion, including perspective distortions, line curvature or other
style variations. Therefore, methods based on spatial transformers are
extensively studied. However, chromatic difficulties in complex scenes have not
been paid much attention on. In this work, we introduce a new learnable
geometric-unrelated module, the Structure-Preserving Inner Offset Network
(SPIN), which allows the color manipulation of source data within the network.
This differentiable module can be inserted before any recognition architecture
to ease the downstream tasks, giving neural networks the ability to actively
transform input intensity rather than the existing spatial rectification. It
can also serve as a complementary module to known spatial transformations and
work in both independent and collaborative ways with them. Extensive
experiments show that the use of SPIN results in a significant improvement on
multiple text recognition benchmarks compared to the state-of-the-arts.

本研究介绍了一种可学习的几何无关模块 ——SPIN，该模块允许神经网络主动变换输入强度颜色，以解决场景文字识别任务中的色彩困难问题，并与现有的空间变换模块协同工作，实验结果表明，使用 SPIN 可以显著提高多个文本识别基准的性能。