The Real First Universal Charset Detector
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:
sh
pip install charset-normalizer -U
This 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. ```
bash
normalizer ./data/sample.1.fr.srt
or
bash
python -m charset_normalizer ./data/sample.1.fr.srt
π Since version 1.4.0 the CLI produce easily usable stdout result in JSON format.
json
{
"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 ```python from charset_normalizer import from_path
results = from_path('./my_subtitle.srt')
print(str(results.best())) ```
Upgrade your code without effort
python
from charset_normalizer import detect
The 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.
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.
If you are running:
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.
All notable changes to charset-normalizer will be documented in this file. This project adheres to Semantic Versioning. The format is based on Keep a Changelog.
--no-preemptive
in the CLI to prevent the detector to search for hints.python -m charset_normalizer.cli
or python -m charset_normalizer
encoding.aliases
as they have no alias (#323)from_path
no longer enforce PathLike
as its first argumentis_binary
that relies on main capabilities, and optimized to detect binariesenable_fallback
argument throughout from_bytes
, from_path
, and from_fp
that allow a deeper control over the detection (default True)should_rename_legacy
for legacy function detect
and disregard any new arguments without errors (PR #262)language_threshold
in from_bytes
, from_path
and from_fp
to adjust the minimum expected coherence rationormalizer --version
now specify if current version provide extra speedup (meaning mypyc compilation whl)md.py
can be compiled using Mypyc to provide an extra speedup up to 4x faster than v2.1first()
and best()
from CharsetMatchnormalize
chaos_secondary_pass
, coherence_non_latin
and w_counter
from CharsetMatchunicodedata2
language_threshold
in from_bytes
, from_path
and from_fp
to adjust the minimum expected coherence rationormalizer --version
now specify if current version provide extra speedup (meaning mypyc compilation whl)first()
and best()
from CharsetMatchmd.py
can be compiled using Mypyc to provide an extra speedup up to 4x faster than v2.1normalize
chaos_secondary_pass
, coherence_non_latin
and w_counter
from CharsetMatchunicodedata2
normalize
scheduled for removal in 3.0--version
(PR #194)unicodedata2
as Python is quickly catching up, scheduled for removal in 3.0 (PR #194)explain
to True (PR #146)NullHandler
by default from @nmaynes (PR #135)explain
to True will add provisionally (bounded to function lifespan) a specific stream handler (PR #135)set_logging_handler
to configure a specific StreamHandler from @nmaynes (PR #135)CHANGELOG.md
entries, format is based on Keep a Changelog (PR #141)alphabets
property. (PR #39)MIT License
Copyright (c) 2019 TAHRI Ahmed R.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.