Today, machine learning algorithms are used to study heart disease. To work at their best performance the algorithms mentioned above need a large amount of data to process. Unfortunately, most of the electrocardiograms (ECGs) collected in the archives by cardiologists is still on paper. Therefore, a preparatory work for the use of the citated algorithms is the creation of a dataset composed of images of leads and their conversion to electrical signals. For this purpose, the ECG-D application was created and this thesis explains its architecture, features, achieved results and limitations. To digitize the ECGs, a digitization process was designed and included in application.