Pure Python Spell Checking based on Peter Norvig’s blog post on setting up a simple spell checking algorithm.
It uses a Levenshtein Distance algorithm to find permutations within an edit distance of 2 from the original word. It then compares all permutations (insertions, deletions, replacements, and transpositions) to known words in a word frequency list. Those words that are found more often in the frequency list are more likely the correct results.
pyspellchecker supports multiple languages including English, Spanish, German, French, and Portuguese. Dictionaries were generated using the WordFrequency project on GitHub.
pyspellchecker supports Python 3 and Python 2.7 but, as always, Python 3 is the preferred version!
pyspellchecker allows for the setting of the Levenshtein Distance to check. For longer words, it is highly recommended to use a distance of 1 and not the default 2. See the quickstart to find how one can change the distance parameter.
Installation
The easiest method to install is using pip:
pip install pyspellchecker
To install from source:
git clone https://github.com/barrust/pyspellchecker.git cd pyspellchecker python setup.py install
As always, it is highly recommend to use the Pipenv package to help manage dependencies!
Quickstart
After installation, using pyspellchecker should be fairly straight forward:
from spellchecker import SpellChecker
spell = SpellChecker()
# find those words that may be misspelled
misspelled = spell.unknown(['something', 'is', 'hapenning', 'here'])
for word in misspelled:
# Get the one `most likely` answer
print(spell.correction(word))
# Get a list of `likely` options
print(spell.candidates(word))
If the Word Frequency list is not to your liking, you can add additional text to generate a more appropriate list for your use case.
from spellchecker import SpellChecker
spell = SpellChecker() # loads default word frequency list
spell.word_frequency.load_text_file('./my_free_text_doc.txt')
# if I just want to make sure some words are not flagged as misspelled
spell.word_frequency.load_words(['microsoft', 'apple', 'google'])
spell.known(['microsoft', 'google']) # will return both now!
If the words that you wish to check are long, it is recommended to reduce the distance to 1. This can be accomplished either when initializing the spell check class or after the fact.
from spellchecker import SpellChecker spell = SpellChecker(distance=1) # set at initialization # do some work on longer words spell.distance = 2 # set the distance parameter back to the default
Additional Methods
correction(word): Returns the most probable result for the misspelled word
candidates(word): Returns a set of possible candidates for the misspelled word
known([words]): Returns those words that are in the word frequency list
unknown([words]): Returns those words that are not in the frequency list
word_probability(word): The frequency of the given word out of all words in the frequency list
