Recent advancements have led to the development of algorithms for interpreting ECG time series. However, the continued prevalence of ECG images (including photo, print screen, or scan from paper-based ECG), especially in the underdeveloped countries, underscores the need for digitization and affordable data analysis to ensure com- prehensive cardiac care and capture the diverse manifes- tations of cardiovascular diseases worldwide. As part of the George B. Moody PhysioNet Challenge 2024, our goal is to propose a series of pragmatic components for the dig- itization and classification of ECG images. First, we gen- erate training samples with the ECG-Image-Kit and re- fine them using diffusion models for data augmentation. Then, we employ a U-net architecture to perform ECG digitization, utilizing this large scale ECG images paired with their corresponding ground-truth time series. Next, we pre-train a RegNet model for ECG classification us- ing a large scale ECG time series data from open-source datasets. This pre-trained classifier is then further fine- tuned with the digitized ECG time series derived from ECG images. Additionally, we devise an adaptable meta-model and a masked training strategy to address issues related to varying lengths and asynchronization when digitizing diverse ECG image layouts. Our team, PKU NIHDS, re- ceives a SNR of -1.103 on the reconstruction task, and F- measure of 0.421 on the classification task on the hidden test set.