git clone https://github.com/pirate/spellchecker
cd spellchecker/
python spellchecker.py

# type interactively to get suggestions
Total Word Set: 285750
Model Precision: 1.62249168854
>manster
[('monster', 2), ('minster', 2)]

# or try some preset mispelled words
python misspeller.py | python spellchecker.py 

You can edit spellchecker.py and add more files to the training list to increase the word-frequency model precision.

Peter Norvig wrote an amazing article titled How to Write a Spelling Corrector detailing a basic approach to this deceivingly simple problem. I had to write a spellchecker as an interview question for Disqus, and this repo details my efforts.

The core code that I borrow from Darius Bacon & Norvig is this beautiful block:

def variants(word):
    """get all possible variants for a word"""
    splits     = [(word[:i], word[i:]) for i in range(len(word) + 1)]
    deletes    = [a + b[1:] for a, b in splits if b]
    transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]
    replaces   = [a + c + b[1:] for a, b in splits for c in alphabet if b]
    inserts    = [a + c + b for a, b in splits for c in alphabet]
    return set(deletes + transposes + replaces + inserts)

Of course that wasn't my only code, I added a lot more on top of Norvig's implementation.

My additions are:

• short-circuiting options for faster checking
• hamming distance and word-frequency model based chooser for suggestions
• double word variants for catching more complex multi-typos
• vowel-swapping detection
• a reductions function to efficiently store word variants like monster: ['m',['o', 'a'], 'n', 's', 't', 'e', 'r']