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<h1 align="center">Charset Detection, for Everyone 👋</h1>

The Real First Universal Charset Detector 
 <a href="https://pypi.org/project/charset-normalizer">
 <img src="https://img.shields.io/pypi/pyversions/charset_normalizer.svg?orange=blue" />
 </a>
 <a href="https://pepy.tech/project/charset-normalizer/">
 <img alt="Download Count Total" src="https://static.pepy.tech/badge/charset-normalizer/month" />
 </a>
 <a href="https://bestpractices.coreinfrastructure.org/projects/7297">
 <img src="https://bestpractices.coreinfrastructure.org/projects/7297/badge">
 </a>


 Featured Packages 
 <a href="https://github.com/jawah/niquests">
 <img alt="Static Badge" src="https://img.shields.io/badge/Niquests-HTTP_1.1%2C%202%2C_and_3_Client-cyan">
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 <a href="https://github.com/jawah/wassima">
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 In other language (unofficial port - by the community) 
 <a href="https://github.com/nickspring/charset-normalizer-rs">
 <img alt="Static Badge" src="https://img.shields.io/badge/Rust-red">
 </a>

> 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.

>>>>> <a href="https://charsetnormalizerweb.ousret.now.sh" target="_blank">👉 Try Me Online Now, Then Adopt Me 👈 </a> <<<<<

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

| Feature | [Chardet](https://github.com/chardet/chardet) | Charset Normalizer | [cChardet](https://github.com/PyYoshi/cChardet) |
|--------------------------------------------------|:---------------------------------------------:|:--------------------------------------------------------------------------------------------------:|:-----------------------------------------------:|
| `Fast` | ❌ | ✅ | ✅ |
| `Universal**` | ❌ | ✅ | ❌ |
| `Reliable` **without** distinguishable standards | ❌ | ✅ | ✅ |
| `Reliable` **with** distinguishable standards | ✅ | ✅ | ✅ |
| `License` | LGPL-2.1 _restrictive_ | MIT | MPL-1.1 _restrictive_ |
| `Native Python` | ✅ | ✅ | ❌ |
| `Detect spoken language` | ❌ | ✅ | N/A |
| `UnicodeDecodeError Safety` | ❌ | ✅ | ❌ |
| `Whl Size (min)` | 193.6 kB | 42 kB | ~200 kB |
| `Supported Encoding` | 33 | 🎉 [99](https://charset-normalizer.readthedocs.io/en/latest/user/support.html#supported-encodings) | 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](https://charset-normalizer.readthedocs.io/en/latest/user/miscellaneous.html)

## ⚡ Performance

This package offer better performance than its counterpart Chardet. Here are some numbers.

| Package                                       | Accuracy | Mean per file (ms) | File per sec (est) |
|-----------------------------------------------|:--------:|:------------------:|:------------------:|
| [chardet](https://github.com/chardet/chardet) |   86 %   |       200 ms       |     5 file/sec     |
| charset-normalizer                            | **98 %** |     **10 ms**      |    100 file/sec    |

| Package                                       | 99th percentile | 95th percentile | 50th percentile |
|-----------------------------------------------|:---------------:|:---------------:|:---------------:|
| [chardet](https://github.com/chardet/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.

## ✨ Installation

Using pip:

```sh
pip install charset-normalizer -U
```

## 🚀 Basic Usage

### CLI
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
```

```bash
python -m charset_normalizer ./data/sample.1.fr.srt
```

🎉 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
}
```

### Python
*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](https://charset-normalizer.readthedocs.io/en/latest/)

## 😇 Why

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 ? 😎

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 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.

## ⚡ Known limitations

- 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.

## ⚠️ About Python EOLs

**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.

## 👤 Contributing

Contributions, issues and feature requests are very much welcome. 
Feel free to check [issues page](https://github.com/ousret/charset_normalizer/issues) if you want to contribute.

## 📝 License

Copyright © [Ahmed TAHRI @Ousret](https://github.com/Ousret). 
This project is [MIT](https://github.com/Ousret/charset_normalizer/blob/master/LICENSE) licensed.

## 💼 For Enterprise

Professional support for charset-normalizer is available as part of the [Tidelift
Subscription][1]. 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.

[1]: https://tidelift.com/subscription/pkg/pypi-charset-normalizer?utm_source=pypi-charset-normalizer&utm_medium=readme