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Description

A library that helps you read text from an unknown charset encoding. Motivated by chardet, I'm trying to resolve the issue by taking a new approach. All IANA character set names for which the Python core library provides codecs are supported.

This project offers you an alternative to Universal Charset Encoding Detector, also known as Chardet.

Around 90 encoding supported out of the box. MIT licensed.

Programming language: Python
License: MIT License
Tags: Text Processing     General     Utilities     Linguistic     Python     Encoding    
Latest version: v1.3.1

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README

Welcome to Charset Detection for Humans πŸ‘‹

The Real First Universal Charset Detector

A library that helps you read text from an unknown charset encoding. Motivated by chardet, I'm trying to resolve the issue by taking a new approach. All IANA character set names for which the Python core library provides codecs are supported.

❀️ Try Me Online Now, Then Adopt Me ❀️ <<<<<

This project offers you an alternative to Universal Charset Encoding Detector, also known as Chardet.

Feature Chardet Charset Normalizer cChardet
Fast ❌ ❌ :heavy_check_mark:
Universal** ❌ :heavy_check_mark: ❌
Reliable without distinguishable standards ❌ :heavy_check_mark: :heavy_check_mark:
Reliable with distinguishable standards :heavy_check_mark: :heavy_check_mark: :heavy_check_mark:
Free & Open :heavy_check_mark: :heavy_check_mark: :heavy_check_mark:
License LGPL-2.1 MIT MPL-1.1
Native Python :heavy_check_mark: :heavy_check_mark: ❌
Detect spoken language ❌ :heavy_check_mark: N/A
Supported Encoding 30 :tada: 90 40
Package Accuracy Mean per file (ns) File per sec (est)
chardet 93.5 % 126 081 168 ns 7.931 file/sec
cchardet 97.0 % 1 668 145 ns 599.468 file/sec
charset-normalizer 97.25 % 209 503 253 ns 4.773 file/sec

** : They are clearly using specific code for a specific encoding even if covering most of used one

Your support

Please ⭐ this repository if this project helped you!

✨ Installation

Using PyPi

pip install charset_normalizer

πŸš€ Basic Usage

CLI

This package comes with a CLI

usage: normalizer [-h] [--verbose] [--normalize] [--replace] [--force]
                  file [file ...]
normalizer ./data/sample.1.fr.srt
+----------------------+----------+----------+------------------------------------+-------+-----------+
|       Filename       | Encoding | Language |             Alphabets              | Chaos | Coherence |
+----------------------+----------+----------+------------------------------------+-------+-----------+
| data/sample.1.fr.srt |  cp1252  |  French  | Basic Latin and Latin-1 Supplement | 0.0 % |  84.924 % |
+----------------------+----------+----------+------------------------------------+-------+-----------+

Python

Just print out normalized text

from charset_normalizer import CharsetNormalizerMatches as CnM
print(CnM.from_path('./my_subtitle.srt').best().first())

Normalize any text file

from charset_normalizer import CharsetNormalizerMatches as CnM
try:
    CnM.normalize('./my_subtitle.srt') # should write to disk my_subtitle-***.srt
except IOError as e:
    print('Sadly, we are unable to perform charset normalization.', str(e))

Upgrade your code without effort

from charset_normalizer import detect

The above code will behave the same as chardet.

See the docs for advanced usage : readthedocs.io

πŸ˜‡ Why

When I started using Chardet, I noticed that it was unreliable nowadays and also it's unmaintained, and most likely will never be.

I don't care about the originating charset encoding, because two different tables can produce two identical files. What I want is to get readable text, the best I can.

In a way, I'm brute forcing text decoding. How cool is that ? 😎

Don't confuse package ftfy with charset-normalizer or chardet. ftfy goal is to repair unicode string whereas charset-normalizer to convert raw file in unknown encoding to unicode.

🍰 How

  • Discard all charset encoding table that could not fit the binary content.
  • Measure chaos, or the mess once opened with a corresponding charset encoding.
  • Extract matches with the lowest mess detected.
  • Finally, if there is too much match left, we measure coherence.

Wait a minute, what is chaos/mess and coherence according to YOU ?

Chaos : I opened hundred of text files, written by humans, with the wrong encoding table. I observed, then I established some ground rules about what is obvious when it seems like a mess. I know that my interpretation of what is chaotic is very subjective, feel free to contribute in order to improve or rewrite it.

Coherence : For each language there is on earth, we have computed ranked letter appearance occurrences (the best we can). So I thought that intel is worth something here. So I use those records against decoded text to check if I can detect intelligent design.

⚑ Known limitations

  • Not intended to work on non (human) speakable language text content. eg. crypted text.
  • Language detection is unreliable when text contains two or more languages sharing identical letters.
  • Not well tested with tiny content.

πŸ‘€ Contributing

Contributions, issues and feature requests are very much welcome. Feel free to check issues page if you want to contribute.

πŸ“ License

Copyright Β© 2019 Ahmed TAHRI @Ousret. This project is MIT licensed.

Letter appearances frequencies used in this project Β© 2012 Denny VrandečiΔ‡


*Note that all licence references and agreements mentioned in the Charset Normalizer README section above are relevant to that project's source code only.