This fixes the broken Russian test in Windows-1251 which once again gets
a much better score with Russian. Also this adds UTF-8 support.
Same as Bulgarian, I wonder why I had not regenerated this earlier.
The new UTF-8 test comes from the 'Сурки' page of Wikipedia in Russian.
Note that now this broke the test zh:gb18030 (the score for KOI8-R / ru
(0.766388) beats GB18030 / zh (0.700000)). I think I'll have to look a
bit closer at our GB18030 dedicated prober.
UTF-8 and Windows-1251 support for now.
This actually breaks ru:windows-1251 test but same as Bulgarian, I never
generated Russian models with my scripts, so the models we currently use
are quite outdated. It will obviously be a lot better once we have new
Russian models.
The test file contents comes from 'Бабак' page on Wikipedia in
Ukrainian.
Not sure why we had the Bulgarian support but haven't recently updated
it (i.e. never with the model generation script, or so it seems),
especially with generic language models, allowing to have
UTF-8/Bulgarian support. Maybe I tested it some time ago and it was
getting bad results? Anyway now with all the recents updates on the
confidence computation, I get very good detection scores.
So adding support for UTF-8/Bulgarian and rebuilding other models too.
Also adding a test for ISO-8859-5/Bulgarian (we already had support, but
no test files).
The 2 new test files are text from page 'Мармоти' on Wikipedia in
Bulgarian language.
Added in both visual and logical order since Wikipedia says:
> Hebrew text encoded using code page 862 was usually stored in visual
> order; nevertheless, a few DOS applications, notably a word processor
> named EinsteinWriter, stored Hebrew in logical order.
I am not using the nsHebrewProber wrapper (nameProber) for this new
support, because I am really unsure this is of any use. Our statistical
code based on letter and sequence usage should be more than enough to
detect both variants of Hebrew encoding already, and my testing show
that so far (with pretty outstanding score on actual Hebrew tests while
all the other probers return bad scores). This will have to be studied a
bit more later and maybe the whole nsHebrewProber might be deleted, even
for Windows-1255 charset.
I'm also cleaning a bit nsSBCSGroupProber::nsSBCSGroupProber() code by
incrementing a single index, instead of maintaining the indexes by hand
(otherwise each time we add probers in the middle, to keep them
logically gathered by languages, we have to manually increment dozens of
following probers).
The previous model was most obviously wrong: all letters had the same
probability, even non-ASCII ones! Anyway this new model does make unit
tests a tiny bit better though the English detection is still weak (I
have more concepts which I want to experiment to get this better).
As I just rebased my branch about new language detection API, I needed
to re-generate Norwegian language models. Unfortunately it doesn't
detect UTF-8 Norwegian text, though not far off (it detects it as second
candidate with high 91% confidence; beaten by Danish UTF-8 with 94%
confidence unfortunately!).
Note that I also update the alphabet list for Norwegian as there were
too many letters in there (according to Wikipedia at least), so even
when training a model, we had some missing characters in the training
set.
English detection is still quite crappy so I don't add a unit test yet.
Though I believe the detection being bad is mostly because of too much
shortcutting we are doing to go "fast". I should probably review this
whole part of the logics as well.
This allows to handle cases where some characters are actually
alternative/variants of another. For instance, a same word can be
written with both variants, while both are considered correct and
equivalent. Browsing a bit Slovenian Wikipedia, it looks like they only
use them for titles there.
I use this the first time on characters with diacritics in Slovene.
Indeed these are so rarely used that they would hardly show in the stats
and worse, any sequence using these in tested text would likely show as
negative sequences hence drop the confidence in Slovenian. As a
consequence, various Slovene text would show up as Slovak as it's close
enough and contains the same character with diacritics in a common way.
The alphabet was not complete and thus confidence was a bit too low.
For instance the VISCII test case's confidence bumped from 0.643401 to
0.696346 and the UTF-8 test case bumped from 0.863777 to 0.99.
Only the Windows-1258 test case is slightly worse from 0.532846 to
0.532098. But the overwhole recognition gain is obvious anyway.
Until now, Korean charsets had its own probers as there are no
single-byte encoding for writing Korean. I now added a Korean model only
for the generic character and sequence statistics.
I also improved the generation script (script/BuildLangModel.py) to
allow for languages without single-byte charset generation and to
provide meaningful statistics even when the language script has a lot of
characters (so we can't have a full sequence combination array, just too
much data). It's not perfect yet. For instance our UTF-8 Korean test
file ends up with confidence of 0.38503, which is low for obvious Korean
text. Still it works (correctly detected, with top confidence compared
to others) and is a first step toward more improvement for detection
confidence.
The Hebrew Model had never been regenerated by my scripts. I now added
the base generation files.
Note that I added 2 charsets: ISO-8859-8 and WINDOWS-1255 but they are
nearly identical. One of the difference is that the generic currency
sign is replaced by the sheqel sign (Israel currency) in Windows-1255.
And though this one lost the "double low line", apparently some Yiddish
characters were added. Basically it looks like most Hebrew text would
work fine with the same confidence on both charsets and detecting both
is likely irrelevant. So I keep the charset file for ISO-8859-8, but
won't actually use it.
The good part is now that Hebrew is also recognized in UTF-8 text thanks
to the new code and newly generated language model.
Adding generic language model (see coming commit), which uses the same
data as specific single-byte encoding statistics model, except that it
applies it to unicode code points.
For this to work, instead of the CharToOrderMap which was mapping
directly from encoded byte (always 256 values) to order, now we add an
array of frequent characters, ordered by generic unicode code points to
the order of frequency (which can be used on the same sequence mapping
array).
This of course means that each prober where we will want to use these
generic models will have to implement their own byte to code point
decoder, as this is per-encoding logics anyway. This will come in a
subsequent commit.
Newly added IBM865 charset (for Norwegian) can also be used for Danish
By the way, I fixed `script/charsets/ibm865.py` as Danish uses the 'da'
ISO 639-1 code by the way, not 'dk' (which is sometimes used for other
codes for Denmark, such as ISO 3166 country code and internet TLD) but
not for the language itself.
For the test, adding some text from the top article of the day on the
Danish Wikipedia, which was about Jimi Hendrix. And that's cool! 🎸 ;-)
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.
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
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.
