aboutsummaryrefslogtreecommitdiffstats
path: root/contrib/python/charset-normalizer/README.md
blob: 904b60ea228d3b76b7e0423dcf5bc439de38aace (plain) (blame)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
<h1 align="center">Charset Detection, for Everyone πŸ‘‹ <a href="https://twitter.com/intent/tweet?text=The%20Real%20First%20Universal%20Charset%20%26%20Language%20Detector&url=https://www.github.com/Ousret/charset_normalizer&hashtags=python,encoding,chardet,developers"><img src="https://img.shields.io/twitter/url/http/shields.io.svg?style=social"/></a></h1>

<p align="center">
  <sup>The Real First Universal Charset Detector</sup><br>
  <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://codecov.io/gh/Ousret/charset_normalizer">
      <img src="https://codecov.io/gh/Ousret/charset_normalizer/branch/master/graph/badge.svg" />
  </a>
  <a href="https://pepy.tech/project/charset-normalizer/">
    <img alt="Download Count Total" src="https://pepy.tech/badge/charset-normalizer/month" />
  </a>
</p>

> A library that helps you read text from an unknown charset encoding.<br /> 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.

<p align="center">
  >>>>> <a href="https://charsetnormalizerweb.ousret.now.sh" target="_blank">πŸ‘‰ Try Me Online Now, Then Adopt Me πŸ‘ˆ </a> <<<<<
</p>

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`         | ❌<br>          | :heavy_check_mark:<br>             | :heavy_check_mark: <br> |
| `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: [93](https://charset-normalizer.readthedocs.io/en/latest/user/support.html#supported-encodings)  | 40

<p align="center">
<img src="https://i.imgflip.com/373iay.gif" alt="Reading Normalized Text" width="226"/><img src="https://media.tenor.com/images/c0180f70732a18b4965448d33adba3d0/tenor.gif" alt="Cat Reading Text" width="200"/>

*\*\* : They are clearly using specific code for a specific encoding even if covering most of used one*<br> 
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)

## ⭐ Your support

*Fork, test-it, star-it, submit your ideas! We do listen.*
  
## ⚑ 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)        |     92 %     |     200 ms      |       5 file/sec        |
| charset-normalizer |    **98 %**     |     **39 ms**      |       26 file/sec    |

| Package       | 99th percentile       | 95th percentile | 50th percentile |
| ------------- | :-------------: | :------------------: | :------------------: |
|      [chardet](https://github.com/chardet/chardet)        |     1200 ms     |     287 ms      |       23 ms        |
| charset-normalizer |    400 ms     |     200 ms      |       15 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.

[cchardet](https://github.com/PyYoshi/cChardet) is a non-native (cpp binding) and unmaintained faster alternative with 
a better accuracy than chardet but lower than this package. If speed is the most important factor, you should try it.

## ✨ Installation

Using PyPi for latest stable
```sh
pip install charset-normalizer -U
```

If you want a more up-to-date `unicodedata` than the one available in your Python setup.
```sh
pip install charset-normalizer[unicode_backport] -U
```

## πŸš€ 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
```

:tada: 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()))
```

*Normalize any text file*
```python
from charset_normalizer import normalize
try:
    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*
```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/)

## πŸ˜‡ 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 chaos, 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 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

  - 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.

## πŸ‘€ Contributing

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

## πŸ“ License

Copyright Β© 2019 [Ahmed TAHRI @Ousret](https://github.com/Ousret).<br />
This project is [MIT](https://github.com/Ousret/charset_normalizer/blob/master/LICENSE) licensed.

Characters frequencies used in this project Β© 2012 [Denny VrandečiΔ‡](http://simia.net/letters/)