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.

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, I highly recommend using the Pipenv package to help manage dependencies!

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

On-line documentation is available; below contains the cliff-notes version of some of the available functions:

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

edit_distance_1(word): Returns a set of all strings at a Levenshtein Distance of one based on the alphabet of the selected language

edit_distance_2(word): Returns a set of all strings at a Levenshtein Distance of two based on the alphabet of the selected language

• Peter Norvig blog post on setting up a simple spell checking algorithm
• hermetdave's WordFrequency project for providing the basis for Non-English dictionaries