Training network to detect spelling mistakes I would like to know what kind of dataset I need (to prepare) for training the network to recognize the spelling mistakes in individual words for English text.

Given the large database of words, having correct one for each incorrect. What kind of input is more efficient for that tasks? Is it using one input per each letter, syllable, whole word or I should use different pattern syllable?

Then the input should be incorrect word, output correct, and if the word doesn't need correction, then both input and output should be the same. Is that the right approach?
I'd personally be more inclined to try longstanding deterministic methods such as Damerau (for typing errors) or Soundex (for homonyms arising from transcribed speech). At the very least, I'd use those as a baseline for any more 'AI-based' approach.