Image generators learn what letters look like, not how words are spelled. The prompt reaches the model as sub-word tokens and small text gets very few pixels, so you often get convincing letter shapes in the wrong order. Newer models are much better at short headlines but still slip on long words, several lines of text and non-Latin scripts.
Re-prompting redraws the whole picture, so you trade one problem for another. Fixing in place keeps the generation you liked: you mark each broken line with a box, type the correct words, and only the inside of those boxes is written back. You never have to type the gibberish itself.