Recognizing disturbed text in real-life images is a difficult problem, as information that is missing due to low-resolution or out-of-focus text has to be recreated. Combining text super-resolution and optical character recognition deep learning models can be a valuable tool to enlarge and enhance text images for better readability, as well as recognize text automatically afterward. We achieve improved peak signal-to-noise ratio and text recognition accuracy scores over a state-of-the-art text super-resolution model TBSRN on the real-world low-resolution dataset TextZoom while having a smaller theoretical model size, due to the usage of quantization techniques. This could allow this model to run on low-power devices. In addition, we show how different training strategies influence the performance of the resulting model.