A majority of visually impaired population of India and other developing economies live in poverty. Accessibility without affordability has little meaning to this population. Assistive technology has great potential to make education accessible to this population, e.g. through refreshable Braille display devices. However, most existing solutions in this space remain out of reach for these users due to high cost. Innovation in data science and software engineering can play an important role in making assistive technological solutions affordable and accessible. In this paper, we present a machine-learning based automated testing approach that has played an important role in enabling us to design one of the most affordable refreshable Braille display devices of the world. The key component of our approach is a visual inspection module (VIM) created using Convolutional Neural Networks (CNNs). In our experiment, our model was able to detect malfunction of a Refreshable Braille display with 97.3% accuracy. Our model is small enough to be run on a battery-powered computer in real- time. Such accurate automatic testing methods have the potential to significantly reduce the cost of RBDs.