This document discusses advances made to two different and challenging areas of visual recognition: Editing multilingual Scene Text and Reconstructing Dynamic Scene Backgrounds. For editing scene text, we present a new framework called FLUX-Text which builds on our previous work with FLUX-Fill by adding new methods to help machines recognise glyphs using both visual and text cues. FLUX Text has been specifically developed for complicated scripts such as those found in non-Latin languages, while offering the same level of generative capability as the more complex FLUX-Fill, requiring only 100,000 training examples to achieve state-of-the-art text fidelity. We have also developed an unsupervised method for automatically removing backgrounds from videos using an autoencoder-based system and reconstructing the background frames as low dimensional manifolds. Our method predicts pixel-wise background noise, enabling adaptive thresholding, without relying on either temporal or motion cues, making it superior to all other available solutions in the areas of CDnet 2014 and LASIESTA when dealing with changes in lighting or camera movement, and providing a consistent level of performance. These advancements made in two different but related fields of research highlight our excellence in accurately editing multilingual text, and reconstructing the backgrounds of dynamic scenes.