The Real First Universal Charset Detector
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In other language (unofficial port - by the community)
A library that helps you read text from an unknown charset encoding.
Motivated bychardet, 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.
>>>>> π Try Me Online Now, Then Adopt Me π <<<<<
This project offers you an alternative to Universal Charset Encoding Detector, also known as Chardet.
| Feature | Chardet | Charset Normalizer | cChardet |
|---|---|---|---|
Fast |
β | β | β |
Universal** |
β | β | β |
Reliable without distinguishable standards |
β | β | β |
Reliable with distinguishable standards |
β | β | β |
License |
LGPL-2.1 restrictive |
MIT | MPL-1.1 restrictive |
Native Python |
β | β | β |
Detect spoken language |
β | β | N/A |
UnicodeDecodeError Safety |
β | β | β |
Whl Size (min) |
193.6 kB | 42 kB | ~200 kB |
Supported Encoding |
33 | π 99 | 40 |
** : They are clearly using specific code for a specific encoding even if covering most of used one
Did you got there because of the logs? See https://charset-normalizer.readthedocs.io/en/latest/user/miscellaneous.html
This package offer better performance than its counterpart Chardet. Here are some numbers.
| Package | Accuracy | Mean per file (ms) | File per sec (est) |
|---|---|---|---|
| chardet | 86 % | 200 ms | 5 file/sec |
| charset-normalizer | 98 % | 10 ms | 100 file/sec |
| Package | 99th percentile | 95th percentile | 50th percentile |
|---|---|---|---|
| chardet | 1200 ms | 287 ms | 23 ms |
| charset-normalizer | 100 ms | 50 ms | 5 ms |
Chardet's performance on larger file (1MB+) are very poor. Expect huge difference on large payload.
Stats are generated using 400+ files using default parameters. More details on used files, see GHA workflows. And yes, these results might change at any time. The dataset can be updated to include more files. The actual delays heavily depends on your CPU capabilities. The factors should remain the same. Keep in mind that the stats are generous and that Chardet accuracy vs our is measured using Chardet initial capability (eg. Supported Encoding) Challenge-them if you want.
Using pip:
pip install charset-normalizer -UThis package comes with a CLI.
usage: normalizer [-h] [-v] [-a] [-n] [-m] [-r] [-f] [-t THRESHOLD]
file [file ...]
The Real First Universal Charset Detector. Discover originating encoding used
on text file. Normalize text to unicode.
positional arguments:
files File(s) to be analysed
optional arguments:
-h, --help show this help message and exit
-v, --verbose Display complementary information about file if any.
Stdout will contain logs about the detection process.
-a, --with-alternative
Output complementary possibilities if any. Top-level
JSON WILL be a list.
-n, --normalize Permit to normalize input file. If not set, program
does not write anything.
-m, --minimal Only output the charset detected to STDOUT. Disabling
JSON output.
-r, --replace Replace file when trying to normalize it instead of
creating a new one.
-f, --force Replace file without asking if you are sure, use this
flag with caution.
-t THRESHOLD, --threshold THRESHOLD
Define a custom maximum amount of chaos allowed in
decoded content. 0. <= chaos <= 1.
--version Show version information and exit.
normalizer ./data/sample.1.fr.srtor
python -m charset_normalizer ./data/sample.1.fr.srtπ Since version 1.4.0 the CLI produce easily usable stdout result in JSON format.
{
"path": "/home/default/projects/charset_normalizer/data/sample.1.fr.srt",
"encoding": "cp1252",
"encoding_aliases": [
"1252",
"windows_1252"
],
"alternative_encodings": [
"cp1254",
"cp1256",
"cp1258",
"iso8859_14",
"iso8859_15",
"iso8859_16",
"iso8859_3",
"iso8859_9",
"latin_1",
"mbcs"
],
"language": "French",
"alphabets": [
"Basic Latin",
"Latin-1 Supplement"
],
"has_sig_or_bom": false,
"chaos": 0.149,
"coherence": 97.152,
"unicode_path": null,
"is_preferred": true
}Just print out normalized text
from charset_normalizer import from_path
results = from_path('./my_subtitle.srt')
print(str(results.best()))Upgrade your code without effort
from charset_normalizer import detectThe above code will behave the same as chardet. We ensure that we offer the best (reasonable) BC result possible.
See the docs for advanced usage : readthedocs.io
When I started using Chardet, I noticed that it was not suited to my expectations, and I wanted to propose a reliable alternative using a completely different method. Also! I never back down on a good challenge!
I don't care about the originating charset encoding, because two different tables can produce two identical rendered string. 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 ? π
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.
- Discard all charset encoding table that could not fit the binary content.
- Measure noise, or the mess once opened (by chunks) with a corresponding charset encoding.
- Extract matches with the lowest mess detected.
- Additionally, we measure coherence / probe for a language.
Wait a minute, what is noise/mess and coherence according to YOU ?
Noise : 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 noise is probably incomplete, 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.
- Language detection is unreliable when text contains two or more languages sharing identical letters. (eg. HTML (english tags) + Turkish content (Sharing Latin characters))
- Every charset detector heavily depends on sufficient content. In common cases, do not bother run detection on very tiny content.
If you are running:
- Python >=2.7,<3.5: Unsupported
- Python 3.5: charset-normalizer < 2.1
- Python 3.6: charset-normalizer < 3.1
- Python 3.7: charset-normalizer < 4.0
Upgrade your Python interpreter as soon as possible.
Contributions, issues and feature requests are very much welcome.
Feel free to check issues page if you want to contribute.
Copyright Β© Ahmed TAHRI @Ousret.
This project is MIT licensed.
Characters frequencies used in this project Β© 2012 Denny VrandeΔiΔ
Professional support for charset-normalizer is available as part of the Tidelift Subscription. Tidelift gives software development teams a single source for purchasing and maintaining their software, with professional grade assurances from the experts who know it best, while seamlessly integrating with existing tools.
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