pyspellchecker

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.

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, I highly recommend using 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!

More work in storing and loading word frequency lists is planned; stay tuned.

Additional Methods

On-line documentation is in the future; until then you can find SpellChecker here:

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

The following are less likely to be needed by the user but are available:

edit_distance_1(word): Returns a set of all strings at a Levenshtein Distance of one

edit_distance_2(word): Returns a set of all strings at a Levenshtein Distance of two