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.
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: 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.
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.
With the new case_mapping lang property, we can consider upper and lower
case versions of the same character as one character.
This makes sense in some language, and would allow to enter some rarer
characters (but still in the main alphabet) inside the frequent
character list. For instance 'œ' and 'Œ' in French.
