Electrocardiograms, or ECGs, are essential for evaluating cardiac function. Most available models focus on digitized ECG data rather than imaging reports, making them unsuitable for under-resourced communities that only have access to paper-based ECG reports. To address this disadvantage, we propose Cardio Care, a mobile solution that processes both images and signals ECGs to detect abnormalities. It utilises Vision Transformer technology to enhance image recognition, making it more applicable for a broader range of scenarios. We use three datasets (public and local datasets) with varying sample sizes and input types to reflect the data in real-world settings. The results show consistent performance across all datasets, emphasizing the potential of Cardio Care to assist cardiologists in remote and resource-limited healthcare facilities. The average macro F1 scores achieved were 65, 99, and 82 for the CPSC, Mendeley, and Cardiometabolic datasets, respectively. This study proposed an alternative to preprocessing images and signals ECGs for a Vision Transformer-based deep learning network, with the inspiring goal of enhancing healthcare access for under-resourced and underserved communities.