Encodings: ISO-8859-4, ISO-8859-13, ISO-8859-13, Windows-1252 and
Windows-1257.
Test text from https://et.wikipedia.org/wiki/Anton_Tšehhov
Windows-1257 and ISO-8859-13 are very close so I added quotation marks
(Jutumärgid) which are on codepoints only present in ISO-8859-13,
making both encoding apart.
This happened when building a Croatian model which can be written with
many different encodings. There were also many irrelevant glyphs (i.e.
used in other languages) in these encodings so we ended with orders over
255, which breaks when converting to unsigned char.
Just let's make sure that we don't cross the 250 limit (over is used for
controls, illegal characters, symbols, numbers…). This means we may have
several characters with order 249, but since orders over the frequent
character list don't matter, this is not a problem.
Officially supported: ISO-8859-1, ISO-8859-3, ISO-8859-9, ISO-8859-15
and WINDOWS-1252. Same as Finnish only ISO-8859-1 and UTF-8 test added
since other encoding end up similar as ISO-8859-1 for most common texts
(i.e. glyphs used in Italian are on the same codepoints on these other
encodings).
Test text from https://it.wikipedia.org/wiki/Architettura_longobarda
I built models for ISO-8859-1, ISO-8859-4, ISO-8859-9, ISO-8859-13,
ISO-8859-15 and WINDOWS-1252, which all contain Finnish letters.
Nevertheless most texts in these encoding end up the same (same
codepoints for the Finnish glyphs) so I keep only tests for ISO-8859-1
and UTF-8. Models for other encoding may still be useful when processing
texts with some symbols, etc.
Encodings are the same as Czech (Windows-1250, ISO-8859-2 and
Mac-CentralEurope) since the resource I found indicate they used the
same encodings historically.
Also it is to be noted that the test examples' encoding were already
properly detected through Czech's models so the languages are definitely
very close, even statistically. Nevertheless adding the right models
will work better and these get better scores. This will take all its
meaning when uchardet will also be used as a language detector (in some
not-too-far future, hopefully!).
Test text taken from: https://sk.wikipedia.org/wiki/Jupiter
Encodings: Windows-1250, ISO-8859-2, IBM852 and Mac-CentralEurope.
Other encodings are known to have been used for Czech: Kamenicky,
KOI-8 CS2 and Cork. But these are uncommon enough that I decided not
to support them (especially since I can't find them supported in iconv
either, or at least not under an alias which I could recognize).
This web page, which contents was made under the Public Domain, is a
good reference for encodings which were used historically for Czech and
Slovak: http://luki.sdf-eu.org/txt/cs-encodings-faq.html
Even the test `if hasattr(page, 'links')` would trigger this exception.
So I try the approach "Easier to Ask Forgiveness than Permission".
Weird stuff but well…
Note: I had this exception when running it on the Maltese data.
Just realizing that these 2 language can also be encoded with these
charsets (even though ISO-8859-13 would appear to be more common…
maybe?). Anyway now the models are updated and can recognize texts
using these encoding for these languages.
Added some test files as well, which work great.
I actually added also couples with ISO-8859-9, ISO-8859-15 and
Windows-1252. Nevertheless there are no differences on the main
characters related to Portuguese so differences will hardly be made
and detection will usually return ISO-8859-1 only.
I did this to improve the model after a user reported a Greek sutitle
badly detected (see commit e0eec3b).
It didn't help, but well... since I updated it with much more data from
Wikipedia. Let's just commit it!
I was planning on adding VISCII support as well, but Python encode()
method does not have any support for it apparently, so I cannot generate
the proper statistics data with the current version of the string.
ISO-8859-11 is basically exactly identical to TIS-620, with the added
non-breaking space character.
Basically our detection will always return TIS-620 except for
exceptional cases when a text has a non-breaking space.
… for langs for which Python lower() algorithm fails.
In particular Turkish dotted/dotless 'i' does not follow same rules
as common western languages.
Lowercase for 'I' is indeed not 'i' but 'ı'.
Uppercase for 'i' is indeed not 'I' but 'İ'.
I had the case with the Turkish dotted 'İ' that lowercasing it with
Python algorithm returned me a decomposed character that it was not able
to recompose. Therefore ord() raised a TypeError because the string
length was 2.
