Abstract
In an era where wearable technology is reshaping applications, Scene Text Detection and Recognition (STDR) becomes a straightforward choice through the lens of egocentric vision. Leveraging Meta's Project Aria smart glasses, this paper investigates how environmental variables, such as lighting, distance, and resolution, affect the performance of state-of-the-art STDR algorithms in real-world scenarios. We introduce a novel, custom-built dataset captured under controlled conditions and evaluate two OCR pipelines: EAST with CRNN, and EAST with PyTesseract. Our findings reveal that resolution and distance significantly influence recognition accuracy, while lighting plays a less predictable role. Notably, image upscaling emerged as a key pre-processing technique, reducing Character Error Rate (CER) from 0.65 to 0.48. We further demonstrate the potential of integrating eye-gaze tracking to optimise processing efficiency by focusing on user attention zones. This work not only benchmarks STDR performance under realistic conditions but also lays the groundwork for adaptive, user-aware AR systems. Our contributions aim to inspire future research in robust, context-sensitive text recognition for assistive and research-oriented applications, such as asset inspection and nutrition analysis. The code is available at this https URL.
Abstract (translated)
在可穿戴技术重塑应用的时代，通过第一人称视角（egocentric vision）来看，场景文本检测和识别（STDR）成为了一个直接的选择。本文利用Meta的Project Aria智能眼镜，探讨了环境变量如光照、距离和分辨率如何影响先进STDR算法在现实世界中的性能表现。我们引入了一套新型的、专门构建的数据集，并在受控条件下对其进行评估，测试了两个OCR流程：EAST与CRNN相结合以及EAST与PyTesseract相结合的方法。 研究发现显示，分辨率和距离显著影响识别准确性，而光照的影响则较为不可预测。值得注意的是，图像放大作为一项关键预处理技术被证明可以降低字符错误率（CER）从0.65降至0.48。我们还展示了整合眼动追踪的潜力，通过关注用户的注意力区域来优化处理效率。 这项工作不仅在现实条件下对STDR性能进行了基准测试，而且还为自适应、用户感知增强现实系统奠定了基础。我们的贡献旨在激发未来研究，在辅助和科研导向应用（如资产检查与营养分析）中开发出更加稳健且符合上下文的文本识别技术。代码可在[此链接](https://example.com)获取。
URL
https://arxiv.org/abs/2507.16330
