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https://github.com/filyp/autocorrect

Spelling corrector in python
https://github.com/filyp/autocorrect

autocorrect autocorrection czech english languages levenshtein-distance multilanguage multilingual nlp ocr polish portuguese python russian spanish spellchecker spelling spelling-corrector turkish ukrainian

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Spelling corrector in python

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README 

        
# Autocorrect

![build](https://github.com/fsondej/autocorrect/workflows/build/badge.svg)

[![Downloads](https://pepy.tech/badge/autocorrect?label=PyPI%20downloads)](https://pepy.tech/project/autocorrect)

[![Average time to resolve an issue](http://isitmaintained.com/badge/resolution/fsondej/autocorrect.svg)](http://isitmaintained.com/project/fsondej/autocorrect "Average time to resolve an issue")

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)



Spelling corrector in python. Currently supports English, Polish, Turkish, Russian, Ukrainian, Czech, Portuguese, Greek, Italian, Vietnamese, French and Spanish, but you can easily add new languages.



Based on: https://github.com/phatpiglet/autocorrect and [Peter Norvig's spelling corrector](https://norvig.com/spell-correct.html).



# Installation

```bash

pip install autocorrect

```



# Examples

```python

>>> from autocorrect import Speller

>>> spell = Speller()

>>> spell("I'm not sleapy and tehre is no place I'm giong to.")

"I'm not sleepy and there is no place I'm going to."



>>> spell = Speller('pl')

>>> spell('ptaaki latatją kluczmm')

'ptaki latają kluczem'

```



# Speed

```python

%timeit spell("I'm not sleapy and tehre is no place I'm giong to.")

373 µs ± 2.09 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit spell("There is no comin to consiousnes without pain.")

150 ms ± 2.02 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

```



As you see, for some words correction can take ~200ms. If speed is important for your use case (e.g. chatbot) you may want to use option 'fast':

```python

spell = Speller(fast=True)

%timeit spell("There is no comin to consiousnes without pain.")

344 µs ± 2.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

```

Now, the correction should always work in microseconds, but words with double typos (like 'consiousnes') won't be corrected.



# OCR

When cleaning up OCR, replacements are the large majority of errors. If this is the case, you may want to use the option 'only_replacements':

```python

spell = Speller(only_replacements=True)

```



# Custom word sets

If you wish to use your own set of words for autocorrection, you can pass an `nlp_data` argument: 



```python

spell = Speller(nlp_data=your_word_frequency_dict)

```

Where `your_word_frequency_dict` is a dictionary which maps words to their average frequencies in your text. If you want to change the default word set only a bit, you can just edit `spell.nlp_data` parameter, after `spell` was initialized.



# Adding new languages

First, define special letters, by adding entries in `word_regexes` and `alphabets` dicts in autocorrect/constants.py.



Now, you need a bunch of text. Easiest way is to download wikipedia.

For example for Russian you would go to:

https://dumps.wikimedia.org/ruwiki/latest/

and download ruwiki-latest-pages-articles.xml.bz2



```

bzip2 -d ruiwiki-latest-pages-articles.xml.bz2

```



After that:



First, edit the `autocorrect.constants` dictionaries in order to accommodate regexes and dictionaries for your language.



Then:



```python

>>> from autocorrect.word_count import count_words

>>> count_words('ruwiki-latest-pages-articles.xml', 'ru')

```



```

tar -zcvf autocorrect/data/ru.tar.gz word_count.json

```



For the correction to work well, you need to cut out rarely used words. First, in test_all.py, write test words for your language, and add them to optional_language_tests the same way as it's done for other languages. It's good to have at least 30 words. Now run:

```

python test_all.py find_threshold ru

```

 and see which threshold value has the least badly corrected words. After that, manually delete all the words with less occurences than the threshold value you found, from the file in hi.tar.gz (it's already sorted so it should be easy).



To distribute this language support to others, you will need to upload your tar.gz file to IPFS (for example with Pinata, which will pin this file so it doesn't disappear), and then add it's path to `ipfs_paths` in `constants.py`. (tip: first put this file inside the folder, and upload the folder to IPFS, for the downloaded file to have the correct filename)



Good luck!