Even though the results obtained by <cite>Yuan et al. (2016)</cite> outperform the previous state-of-the-art, neither the used datasets nor the constructed models are available to the community. This is unfortunate because this makes the re-application of this technique a non-trivial process, and it hinders further studies for understanding which limitations prevent even higher accuracies. These could be, for instance, of algorithmic nature or relate to the input (either size or quality), and a deeper understanding is crucial for enabling further improvements. In addition, some details are not reported, and this could prevent other attempts from replicating the results. To address these issues, we reimplemented <cite>Yuan et al. (2016)</cite> 's method with the goal of: 1) reproducing and making available the code, trained models, and results and 2) understanding which are the main factors that constitute the strengths and weaknesses of this method.