Millions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1,596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains. The model improves on the state-of-the-art in all subcategories. The full software is released as open-source, promoting reproducibility and further development. We hope the software will contribute to unlocking retrospective ECG archives and democratize access to AI-driven diagnostics.

翻译：数百万份临床心电图仅以纸质扫描形式存在，无法用于现代自动化诊断。我们提出了一种全自动模块化框架，可将扫描或拍摄的心电图图像转换为适用于临床和研究应用的数字信号。该框架在37,191份心电图图像上得到验证，其中1,596份采集自阿克什胡斯大学医院。在存在常见伪影的扫描纸质心电图上，该算法获得的平均信噪比为19.65 dB。该框架进一步在埃默里纸质数字化心电图数据集（包含35,595张图像，涵盖透视畸变、褶皱和污渍等复杂情况）上进行了评估。该模型在所有子类别中均优于现有最优方法。完整软件已作为开源项目发布，以促进可重复性和后续发展。我们期望该软件能够助力解锁历史心电图档案库，并推动人工智能诊断技术的普及化应用。