Actually I tend to disagree. Yeah, you can do that for a hobby project.
I'm working in OCR / ICR here. We're often depending on dictionaries to fix recognition errors. So you want to correct names for example, by creating a more or less complete list of all names (we're assuming you know all the names in advance, for example for customers). The dictionary is huge.
"Looking up a word in this hash table dictionary is a trivial expression, one built into the language. And that's it."
Well, no. A spellchecker that just answers "I don't know that word" would be crap. You need to find similar words. Which isn't part of any standard library I am familiar with.
In addition it's still a challenge to find all candidates that have a levenshtein distance (or LD or .. whatever metric you like) of N from the input, for N > 2 or 3.
Think address. You have a list of all streets, which tend to be rather long (Germany: "Strasse" = street, the pattern "<name> Strasse" is common, let's assume you expect 12-14+ chars). The longer the expected input, the higher I'd set the tolerance for errors. Finding all matches with 3+ substitutions/deletions/additions on a large dataset, in a small amount of time is still interesting and a challenge.
In fact, we partner with one company that provides nothing but exactly this, as their single (well-known, respected) product.
You should probably also go Bayesian on word frequencies for the suggestion ordering. Maybe also on error types (missing letter, repeated letter, nearby (on standard keyboard) letter.
Your comment and the reply to it are good if we're talking spell checker software for stuff people type.
What I have to deal with as well are recognition errors. From trivial replacements (I/l/1,C/G,i/j,P/R etc.) to 'engine said that there's some character here according to the character segmentation results, but it has no damn clue what character it could be' that's a different look at the same problem.
Also, for typed content I see quite a lot of shortcuts. SwiftKey for Android, nice as it is, seems to be too braindead to correct words that _start_ with the wrong character (if I type vharacter, with just one single slip at the start, it fails miserably and looks baaad imho). Assumptions like 'just check adjacent characters on the keyboard (special points for assuming a keyboard layout)' or 'ah, the user is certainly starting the jnput correctly and just might misspress a letter in the middle of a word' are nice models to make the developer's life easier, but they don't work for a good number of cases as well..
I used soundex and metaphone to correct spelling mistakes for a project. My assumption: that most spelling mistakes are people trying to sound out the spelling and not simple typos. For the limited case I was solving (mostly Biblical names) it worked quite well.
I'm working in OCR / ICR here. We're often depending on dictionaries to fix recognition errors. So you want to correct names for example, by creating a more or less complete list of all names (we're assuming you know all the names in advance, for example for customers). The dictionary is huge.
"Looking up a word in this hash table dictionary is a trivial expression, one built into the language. And that's it."
Well, no. A spellchecker that just answers "I don't know that word" would be crap. You need to find similar words. Which isn't part of any standard library I am familiar with. In addition it's still a challenge to find all candidates that have a levenshtein distance (or LD or .. whatever metric you like) of N from the input, for N > 2 or 3.
Think address. You have a list of all streets, which tend to be rather long (Germany: "Strasse" = street, the pattern "<name> Strasse" is common, let's assume you expect 12-14+ chars). The longer the expected input, the higher I'd set the tolerance for errors. Finding all matches with 3+ substitutions/deletions/additions on a large dataset, in a small amount of time is still interesting and a challenge.
In fact, we partner with one company that provides nothing but exactly this, as their single (well-known, respected) product.