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
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
candidates(word): Returns a set of possible candidates for the
known([words]): Returns those words that are in the word frequency
unknown([words]): Returns those words that are not in the frequency
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 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