At work it seems like no week ever passes without some encoding-related conniption, calamity, or catastrophe. The problem usually derives from programmers who think they can reliably process a “text” file without specifying the encoding. But you can’t.
So it’s been decided to henceforth forbid files from ever having names that end in
*.text. The thinking is that those extensions mislead the casual programmer into a dull complacency regarding encodings, and this leads to improper handling. It would almost be better to have no
extension at all, because at least then you know that you don’t know what you’ve got.
However, we aren’t goint to go that far. Instead you will be expected to use a filename that ends in the encoding. So for text files, for example, these would be something like
For files that demand a particular extension, if one can specify the encoding inside the file itself, such as in Perl or Python, then you shall do that. For files like Java source where no such facility exists internal to the file, you will put the encoding before the extension, such as
For output, UTF-8 is to be strongly preferred.
But for input, we need to figure out how to deal with the thousands of files in our codebase named
*.txt. We want to rename all of them to fit into our new standard. But we can’t possibly eyeball them all. So we need a library or program that actually works.
These are variously in ASCII, ISO-8859-1, UTF-8, Microsoft CP1252, or Apple MacRoman. Although we’re know we can tell if something is ASCII, and we stand a good change of knowing if something is probably UTF-8, we’re stumped about the 8-bit encodings. Because we’re running in a mixed Unix environment (Solaris, Linux, Darwin) with most desktops being Macs, we have quite a few annoying MacRoman files. And these especially are a problem.
For some time now I’ve been looking for a way to programmatically determine which of
a file is in, and I haven’t found a program or library that can reliably distinguish between those the three different 8-bit encodings. We probably have over a thousand MacRoman files alone, so whatever charset detector we use has to be able to sniff those out. Nothing I’ve looked at can manage the trick. I had big hopes for the ICU charset detector library, but it cannot handle MacRoman. I’ve also looked at modules to do the same sort of thing in both Perl and Python, but again and again it’s always the same story: no support for detecting MacRoman.
What I am therefore looking for is an existing library or program that reliably determines which of those five encodings a file is in—and preferably more than that. In particular it has to distinguish between the three 3-bit encoding I’ve cited, especially MacRoman. The files are more than 99% English language text; there are a few in other languages, but not many.
If it’s library code, our language preference is for it to be in Perl, C, Java, or Python, and in that order. If it’s just a program, then we don’t really care what language it’s in so long as it comes in full source, runs on Unix, and is fully unencumbered.
Has anyone else had this problem of a zillion legacy text files randomly encoded? If so, how did you attempt to solve it, and how successful were you? This is the most important aspect of my question, but I’m also interested in whether you think encouraging programmers to name (or rename) their files with the actual encoding those files are in will help us avoid the problem in the future. Has anyone ever tried to enforce this on an institutional basis, and if so, was that successful or not, and why?
And yes, I fully understand why one cannot guarantee a definite answer given the nature of the problem. This is especially the case with small files, where you don’t have enough data to go on. Fortunately, our files are seldom small. Apart from the random
README file, most are in the size range of 50k to 250k, and many are larger. Anything more than a few K in size is guaranteed to be in English.
The problem domain is biomedical text mining, so we sometimes deal with extensive and extremely large corpora, like all of PubMedCentral’s Open Access respository. A rather huge file is the BioThesaurus 6.0, at 5.7 gigabytes. This file is especially annoying because it is almost all UTF-8. However, some numbskull went and stuck a few lines in it that are in some 8-bit encoding—Microsoft CP1252, I believe. It takes quite a while before you trip on that one. 🙁
First, the easy cases:
If your data contains no bytes above 0x7F, then it’s ASCII. (Or a 7-bit ISO646 encoding, but those are very obsolete.)
If your data validates as UTF-8, then you can safely assume it is UTF-8. Due to UTF-8’s strict validation rules, false positives are extremely rare.
The only difference between these two encodings is that ISO-8859-1 has the C1 control characters where windows-1252 has the printable characters €‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžŸ. I’ve seen plenty of files that use curly quotes or dashes, but none that use C1 control characters. So don’t even bother with them, or ISO-8859-1, just detect windows-1252 instead.
That now leaves you with only one question.
This is a lot trickier.
The bytes 0x81, 0x8D, 0x8F, 0x90, 0x9D are not used in windows-1252. If they occur, then assume the data is MacRoman.
The bytes 0xA2 (¢), 0xA3 (£), 0xA9 (©), 0xB1 (±), 0xB5 (µ) happen to be the same in both encodings. If these are the only non-ASCII bytes, then it doesn’t matter whether you choose MacRoman or cp1252.
Count character (NOT byte!) frequencies in the data you know to be UTF-8. Determine the most frequent characters. Then use this data to determine whether the cp1252 or MacRoman characters are more common.
For example, in a search I just performed on 100 random English Wikipedia articles, the most common non-ASCII characters are
·•–é°®’èö—. Based on this fact,
Count up the cp1252-suggesting bytes and the MacRoman-suggesting bytes, and go with whichever is greatest.