Text recognition in real-life images poses a challenging task due to blur, distortion, and low resolution. This work presents an innovative method integrating image super-resolution, image restoration, and optical character recognition techniques to enhance text recognition in real-life photographs. We specifically reviewed the processing of the TextZoom dataset and utilized transfer learning on an improved version of the image super-resolution model, SwinIR. The findings of our experiment show that our text recognition scores are better than the current best scores, and there is a significant rise in the peak signal-to-noise ratio while dealing with deformed low-resolution images from the TextZoom dataset. This approach outperforms earlier research in the domain of scene text image super-resolution and offers a promising resolution for text recognition in real-life images. The code can be accessed at this location: https://github.com/Phimanu/TextSR

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 afterwards. 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. In addition, we show how different training strategies influence the performance of the resulting model.