Or how I used brute-force where I least expected it
There is a very old issue regarding “encoding detection” in a text file that has been partially resolved by a program like Chardet. I did not like the idea of single prober per encoding table that could lead to hard coding specifications.
I wanted to challenge the existing methods of discovering originating encoding.
You could consider this issue as obsolete because of current norms :
You should indicate used charset encoding as described in standards
But the reality is different, a huge part of the internet still have content with an unknown encoding. ( One could point out subrip subtitle (SRT) for instance )
This is why a popular package like psf/Requests embed Chardet to guess apparent encoding on remote resources.
You should know that :
- You should not care about the originating charset encoding, that because two different table can produce two identical files.
I’m brute-forcing on three premises :
- Binaries fit encoding table
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 to improve or rewrite it.
Coherence : For each language there is on earth (the best we can), we have computed letter appearance occurrences ranked. So I thought that those intel are worth something here. So I use those records against the decoded text to check if I can detect intelligent design.
So I present to you Charset Normalizer. The Real First Universal Charset Detector
Feel free to help us though testing or contributing.