What I wanted to do here is to develop, in less than a page of code, a toy spelling corrector that achieves 80 or 90% accuracy at a processing speed of at least 10 words per second.

So here, in 21 lines of Python 2.5 code, is the complete spelling corrector:

import re, collections

def words(text): return re.findall('[a-z]+', text.lower())

def train(features):
model = collections.defaultdict(lambda: 1)
for f in features:
model[f] += 1
return model

NWORDS = train(words(file('big.txt').read()))

alphabet = 'abcdefghijklmnopqrstuvwxyz'

def edits1(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)

def known_edits2(word):
return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)

def known(words): return set(w for w in words if w in NWORDS)

def correct(word):
candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
return max(candidates, key=NWORDS.get)
The code defines the function correct, which takes a word as input and returns a likely correction of that word. For example:

>>> correct('speling')
'spelling'
>>> correct('korrecter')
'corrector'