# Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research

arXiv: https://arxiv.org/abs/2510.19590 · HTML: https://mlsanity.com/paper/2510.19590/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)
- [Segmentation Loss Odyssey](https://mlsanity.com/paper/2005.13449/) (2020)
- [Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts](https://mlsanity.com/paper/2410.14185/) (2024)
- [ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis](https://mlsanity.com/paper/2409.16612/) (2024)

## Built on by (most cited first, 3 of 3)

- [Knowledge Knows, Verbalization Tells: Disentangling Latent Directions for Mathematical Solvability in LLMs](https://mlsanity.com/paper/2607.05013/) (2026)
- [VLM-in-the-Loop: A Plug-In Quality Assurance Module for ECG Digitization Pipelines](https://mlsanity.com/paper/2604.00396/) (2026)
- [Learning ECG Image Representations via Dual Physiological-Aware Alignments](https://mlsanity.com/paper/2604.01526/) (2026)

## Abstract (arXiv)

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.