Despite advances in digital storage of electrocardiograms (ECGs), paper print outs are still common place in clinical practice. The digitization and interpretation of paper ECGs is therefore of high utility. We describe the creation of a modular pipeline to achieve both of these tasks. The solution was created by the Easy Geese for the Digitization and Classification of ECG Images: George B. Moody PhysioNet Challenge 2024. Methods: The pipeline accepts an image of a 12-lead ECG in any common format. It first extracts the area of interest using YOLO, and then segments pixels that constitute the ECG signals using a ResUnet. The resulting mask is rotated, and contiguous signal pixels are joined within the area of interest. In the last part of digitization, the signals are scaled, separated by lead, and checked for errors. Finally, the digitized 12-lead signals are input into an SEresnet classifier to provide clinical interpretation. Results: Our ResUnet had a Dice score of 0.997. On the test set, our digitization pipeline had an average signal-tonoise ratio (SNR) of −5.272; our ECG classifier had a macro F-measure of 0.082. This entry was not ranked in the official phase but in the hackathon, where we ranked 2/2 and 1/1 on digitization and classification, respectively.