Introduction: Electrocardiograms (ECGs) exist in both paper and digital formats. While digital ECGs offer ease of analysis and categorization, paper ECGs are still widely used. The PhysioNet Challenge 2024 aims to digitize and classify ECGs captured from images or paper printouts. Method: YOLOv8 Tiny was utilized for image correction. The YOLO identified the tilt angle between the upper and lower lead names. And the angle was used to correct the image. For the digital task, a ResUNet model with an integrated CBAM module was employed. A novel method was proposed to process input and output images, followed by a column-by-column scanning of the binary output image to derive one-dimensional signals. For the classification task, ResNet50 was utilized. ECGs were classified based on the predicted values of 11 output classes. Result: Our team, USST_Med, received a challenging score of an SNR of 2.202 for the digital task and an F-measure score of 0.393 for the classification task. Conclusion: The proposed approaches had good digitalization and classification effects on paper ECGs.