Pure python spell checker based on work by Peter Norvig
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, and French. Dictionaries were generated using the WordFrequency project on GitHub.
pyspellchecker supports Python 3. If may work for Python 2.7 but it is not guaranteed (especially for Non-English dictionaries)!
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!
More work in storing and loading word frequency lists is planned; stay tuned.
On-line documentation is in the future; until then you can find more information on 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 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