Cardiovascular diseases (CVDs) are the leading causes of global mortality and require an early and precise diagnosis. This work presents an automated multi-class classifier for diagnosing cardiac disease from electrocardiogram (ECG) images through image processing and machine-learning techniques. The proposed framework consists of three steps, including pre-processing, feature extraction, and ensemble learning. Initially, the ECG image undergoes a comprehensive pre-processing pipeline that includes lead segmentation, grayscale conversion, Gaussian filtering, and Otsu thresholding. The contour-based features are extracted and then reduced by PCA to preserve discriminative information. Finally, multiple machine-learning models, including K-nearest neighbors (KNNs), Random Forest and support vector machines (SVMs), are ensembled using voting and stacking classifiers to improve the performance of the proposed framework. The proposed ensemble model is evaluated on a public dataset that consists of ECG images that are categorized into four classes: normal, abnormal, myocardial infarction (MI), and history of MI. The proposed ensemble model attained the highest classification accuracy of 98.06% and outperformed the existing pre-trained and state-of-the-art models.