These ideas are extremely powerful. I built a spell-checker largely based on this article, by parsing English Wikipedia. At scale it needs a trie and a distance metric with better-scaling performance metric, but it works really well. These go together: your error metric is a priority-based multi-headed traversal of your trie -- this emulates comparing your word to every word in your dictionary; you can compare against a few million words very quickly.

Because it's based on Wikipedia, it can correct proper names too. It's very extensible: by changing the error model, you can get fuzzy auto-complete that can look at "Shwarz" and suggest "Schwarzenegger" (even though you missed the 'c'). You can extend it to looking at multiple words instead of just letters, for correcting common word-use issues as well.