… 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.
Control characters are not an error per-se. Nevertheless they are clearly not
frequent in single-byte charset texts. It is only normal for them to lower
confidence in a charset. In particular a higher ctrl-per-letter ratio means
a lower confidence.
This fixes for instance our Windows-1252 German test (otherwise detected as
ISO-8859-1).
Let's shortcut Single Byte charset detection on invalid codepoints.
Merging and fixing the contributor's commit conflicts after code
redesign: in particular we added an illegal character concept (they were
mixed with control characters in current charmaps. Yet ctrl characters
are NOT to be considered invalid) and constants instead of hardcoded
numbers ('ILL' rather than 255).
If all sequences in a text are positive sequences, the ratio of positive
sequences cannot make the difference between 2 very close charsets.
A ratio of positive sequences per letters on the other hand will
change a tie between 2 encoding. If while adding a letter, the number
of positive sequences does not increase, the confidence will decrease
(corresponding to the fact it was likely not a letter).
On the other hand, if the number of positive sequences increase, so
will the confidence.
For instance this fixes wrong detections of ISO-8859-1 and ISO-8859-15.
When letters only available in ISO-8859-15 appear in a text, we expect
confidence to tilt towards the close yet slightly different ISO-8859-15.
I.e. horizontally or "breadth first" rather than vertical tree traversal.
This allows to make sure all the start pages in particular are searched,
when using max_page option.
Previous technical text about charsets themselves were not relevant
to identify a language. In particular the special characters different
between ISO-8859-1 and ISO-8859-15 were used by themselves, out of a
char sequence context. Therefore without language understanding, they
could have as well been representing the ISO-8859-15 letters or the
ISO-8859-1 symbols at the corresponding codepoints.
Replacing with text from this Wikipedia page:
https://fr.wikipedia.org/wiki/Œuf_(cuisine)
This uses some of these same characters (in particular 'œ') but in
contextual character sequences, making it relevant for our algorithm.
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.
The lib used to return "" for both properly detected ASCII and
detection failure. And the tool would return "ascii/unknown".
Make a proper distinction between the 2 cases.
Mostly generated with a script from Wikipedia data (only the typical
positive ratio is slightly modified).
This is a first test before adding my generating script to the main tree.
