As part of the George B. Moody PhysioNet Challenge 2024, we developed a deep learning model based on detection and segmentation to recover electrocardiogram (ECG) time series from ECG record printouts. Our team, mins-eth, designed a hybrid pipeline of convolutional neural networks (CNNs) that leverages the strength of Faster Region-based Convolutional Neural Network (Faster R-CNN) for precise detection of the signals and that of U-Net for pixel-level accurate segmentation. Our model can handle a variety of distortions present in scanned ECG records, including rotation, cropping, creases, as well as text artifacts, and efficiently identifies and extracts ECG waveforms. For the digitization task, our model received an SNR of 0.541 (ranked 8th) on the hidden validation set.