标题： 场景文本检测与识别 使用Aria眼镜第一视角视觉相机在具有挑战性的环境条件下的情况 显示英文标题

标题： Scene Text Detection and Recognition "in light of" Challenging Environmental Conditions using Aria Glasses Egocentric Vision Cameras

摘要： 在可穿戴技术重塑应用的时代，通过第一视角视觉，场景文本检测与识别（STDR）成为一种直接的选择。利用Meta的Project Aria智能眼镜，本文研究了环境变量，如光照、距离和分辨率，如何影响最先进的STDR算法在现实场景中的性能。我们引入了一个新型的、定制构建的数据集，在受控条件下采集，并评估了两种OCR流程：EAST结合CRNN，以及EAST结合PyTesseract。我们的研究结果表明，分辨率和距离显著影响识别准确性，而光照的作用则不太可预测。值得注意的是，图像上采样作为一种关键的预处理技术，将字符错误率（CER）从0.65降低到0.48。我们进一步展示了整合眼动追踪以优化处理效率的潜力，通过专注于用户关注区域。这项工作不仅在现实条件下基准测试了STDR的性能，还为自适应、用户感知的增强现实系统奠定了基础。我们的贡献旨在激发未来在辅助和研究导向应用中稳健、上下文敏感的文本识别研究，例如资产检查和营养分析。代码可在https://github.com/josepDe/Project_Aria_STR获取。 显示英文摘要

摘要： 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 https://github.com/josepDe/Project_Aria_STR.