forked from s434650/CatOrNot
293 lines
14 KiB
Python
293 lines
14 KiB
Python
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######################## BEGIN LICENSE BLOCK ########################
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# The Original Code is Mozilla Universal charset detector code.
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#
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# The Initial Developer of the Original Code is
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# Shy Shalom
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# Portions created by the Initial Developer are Copyright (C) 2005
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# the Initial Developer. All Rights Reserved.
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#
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# Contributor(s):
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# Mark Pilgrim - port to Python
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#
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# This library is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Lesser General Public
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# License as published by the Free Software Foundation; either
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# version 2.1 of the License, or (at your option) any later version.
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#
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# This library is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this library; if not, write to the Free Software
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# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
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# 02110-1301 USA
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######################### END LICENSE BLOCK #########################
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from .charsetprober import CharSetProber
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from .enums import ProbingState
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# This prober doesn't actually recognize a language or a charset.
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# It is a helper prober for the use of the Hebrew model probers
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### General ideas of the Hebrew charset recognition ###
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#
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# Four main charsets exist in Hebrew:
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# "ISO-8859-8" - Visual Hebrew
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# "windows-1255" - Logical Hebrew
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# "ISO-8859-8-I" - Logical Hebrew
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# "x-mac-hebrew" - ?? Logical Hebrew ??
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#
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# Both "ISO" charsets use a completely identical set of code points, whereas
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# "windows-1255" and "x-mac-hebrew" are two different proper supersets of
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# these code points. windows-1255 defines additional characters in the range
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# 0x80-0x9F as some misc punctuation marks as well as some Hebrew-specific
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# diacritics and additional 'Yiddish' ligature letters in the range 0xc0-0xd6.
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# x-mac-hebrew defines similar additional code points but with a different
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# mapping.
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#
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# As far as an average Hebrew text with no diacritics is concerned, all four
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# charsets are identical with respect to code points. Meaning that for the
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# main Hebrew alphabet, all four map the same values to all 27 Hebrew letters
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# (including final letters).
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#
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# The dominant difference between these charsets is their directionality.
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# "Visual" directionality means that the text is ordered as if the renderer is
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# not aware of a BIDI rendering algorithm. The renderer sees the text and
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# draws it from left to right. The text itself when ordered naturally is read
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# backwards. A buffer of Visual Hebrew generally looks like so:
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# "[last word of first line spelled backwards] [whole line ordered backwards
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# and spelled backwards] [first word of first line spelled backwards]
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# [end of line] [last word of second line] ... etc' "
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# adding punctuation marks, numbers and English text to visual text is
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# naturally also "visual" and from left to right.
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#
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# "Logical" directionality means the text is ordered "naturally" according to
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# the order it is read. It is the responsibility of the renderer to display
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# the text from right to left. A BIDI algorithm is used to place general
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# punctuation marks, numbers and English text in the text.
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#
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# Texts in x-mac-hebrew are almost impossible to find on the Internet. From
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# what little evidence I could find, it seems that its general directionality
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# is Logical.
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#
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# To sum up all of the above, the Hebrew probing mechanism knows about two
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# charsets:
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# Visual Hebrew - "ISO-8859-8" - backwards text - Words and sentences are
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# backwards while line order is natural. For charset recognition purposes
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# the line order is unimportant (In fact, for this implementation, even
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# word order is unimportant).
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# Logical Hebrew - "windows-1255" - normal, naturally ordered text.
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#
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# "ISO-8859-8-I" is a subset of windows-1255 and doesn't need to be
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# specifically identified.
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# "x-mac-hebrew" is also identified as windows-1255. A text in x-mac-hebrew
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# that contain special punctuation marks or diacritics is displayed with
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# some unconverted characters showing as question marks. This problem might
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# be corrected using another model prober for x-mac-hebrew. Due to the fact
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# that x-mac-hebrew texts are so rare, writing another model prober isn't
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# worth the effort and performance hit.
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#
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#### The Prober ####
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#
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# The prober is divided between two SBCharSetProbers and a HebrewProber,
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# all of which are managed, created, fed data, inquired and deleted by the
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# SBCSGroupProber. The two SBCharSetProbers identify that the text is in
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# fact some kind of Hebrew, Logical or Visual. The final decision about which
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# one is it is made by the HebrewProber by combining final-letter scores
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# with the scores of the two SBCharSetProbers to produce a final answer.
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#
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# The SBCSGroupProber is responsible for stripping the original text of HTML
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# tags, English characters, numbers, low-ASCII punctuation characters, spaces
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# and new lines. It reduces any sequence of such characters to a single space.
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# The buffer fed to each prober in the SBCS group prober is pure text in
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# high-ASCII.
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# The two SBCharSetProbers (model probers) share the same language model:
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# Win1255Model.
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# The first SBCharSetProber uses the model normally as any other
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# SBCharSetProber does, to recognize windows-1255, upon which this model was
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# built. The second SBCharSetProber is told to make the pair-of-letter
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# lookup in the language model backwards. This in practice exactly simulates
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# a visual Hebrew model using the windows-1255 logical Hebrew model.
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#
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# The HebrewProber is not using any language model. All it does is look for
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# final-letter evidence suggesting the text is either logical Hebrew or visual
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# Hebrew. Disjointed from the model probers, the results of the HebrewProber
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# alone are meaningless. HebrewProber always returns 0.00 as confidence
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# since it never identifies a charset by itself. Instead, the pointer to the
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# HebrewProber is passed to the model probers as a helper "Name Prober".
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# When the Group prober receives a positive identification from any prober,
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# it asks for the name of the charset identified. If the prober queried is a
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# Hebrew model prober, the model prober forwards the call to the
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# HebrewProber to make the final decision. In the HebrewProber, the
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# decision is made according to the final-letters scores maintained and Both
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# model probers scores. The answer is returned in the form of the name of the
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# charset identified, either "windows-1255" or "ISO-8859-8".
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class HebrewProber(CharSetProber):
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# windows-1255 / ISO-8859-8 code points of interest
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FINAL_KAF = 0xea
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NORMAL_KAF = 0xeb
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FINAL_MEM = 0xed
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NORMAL_MEM = 0xee
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FINAL_NUN = 0xef
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NORMAL_NUN = 0xf0
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FINAL_PE = 0xf3
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NORMAL_PE = 0xf4
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FINAL_TSADI = 0xf5
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NORMAL_TSADI = 0xf6
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# Minimum Visual vs Logical final letter score difference.
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# If the difference is below this, don't rely solely on the final letter score
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# distance.
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MIN_FINAL_CHAR_DISTANCE = 5
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# Minimum Visual vs Logical model score difference.
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# If the difference is below this, don't rely at all on the model score
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# distance.
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MIN_MODEL_DISTANCE = 0.01
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VISUAL_HEBREW_NAME = "ISO-8859-8"
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LOGICAL_HEBREW_NAME = "windows-1255"
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def __init__(self):
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super(HebrewProber, self).__init__()
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self._final_char_logical_score = None
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self._final_char_visual_score = None
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self._prev = None
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self._before_prev = None
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self._logical_prober = None
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self._visual_prober = None
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self.reset()
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def reset(self):
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self._final_char_logical_score = 0
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self._final_char_visual_score = 0
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# The two last characters seen in the previous buffer,
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# mPrev and mBeforePrev are initialized to space in order to simulate
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# a word delimiter at the beginning of the data
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self._prev = ' '
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self._before_prev = ' '
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# These probers are owned by the group prober.
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def set_model_probers(self, logicalProber, visualProber):
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self._logical_prober = logicalProber
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self._visual_prober = visualProber
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def is_final(self, c):
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return c in [self.FINAL_KAF, self.FINAL_MEM, self.FINAL_NUN,
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self.FINAL_PE, self.FINAL_TSADI]
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def is_non_final(self, c):
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# The normal Tsadi is not a good Non-Final letter due to words like
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# 'lechotet' (to chat) containing an apostrophe after the tsadi. This
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# apostrophe is converted to a space in FilterWithoutEnglishLetters
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# causing the Non-Final tsadi to appear at an end of a word even
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# though this is not the case in the original text.
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# The letters Pe and Kaf rarely display a related behavior of not being
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# a good Non-Final letter. Words like 'Pop', 'Winamp' and 'Mubarak'
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# for example legally end with a Non-Final Pe or Kaf. However, the
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# benefit of these letters as Non-Final letters outweighs the damage
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# since these words are quite rare.
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return c in [self.NORMAL_KAF, self.NORMAL_MEM,
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self.NORMAL_NUN, self.NORMAL_PE]
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def feed(self, byte_str):
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# Final letter analysis for logical-visual decision.
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# Look for evidence that the received buffer is either logical Hebrew
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# or visual Hebrew.
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# The following cases are checked:
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# 1) A word longer than 1 letter, ending with a final letter. This is
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# an indication that the text is laid out "naturally" since the
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# final letter really appears at the end. +1 for logical score.
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# 2) A word longer than 1 letter, ending with a Non-Final letter. In
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# normal Hebrew, words ending with Kaf, Mem, Nun, Pe or Tsadi,
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# should not end with the Non-Final form of that letter. Exceptions
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# to this rule are mentioned above in isNonFinal(). This is an
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# indication that the text is laid out backwards. +1 for visual
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# score
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# 3) A word longer than 1 letter, starting with a final letter. Final
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# letters should not appear at the beginning of a word. This is an
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# indication that the text is laid out backwards. +1 for visual
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# score.
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#
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# The visual score and logical score are accumulated throughout the
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# text and are finally checked against each other in GetCharSetName().
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# No checking for final letters in the middle of words is done since
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# that case is not an indication for either Logical or Visual text.
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#
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# We automatically filter out all 7-bit characters (replace them with
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# spaces) so the word boundary detection works properly. [MAP]
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if self.state == ProbingState.NOT_ME:
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# Both model probers say it's not them. No reason to continue.
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return ProbingState.NOT_ME
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byte_str = self.filter_high_byte_only(byte_str)
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for cur in byte_str:
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if cur == ' ':
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# We stand on a space - a word just ended
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if self._before_prev != ' ':
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# next-to-last char was not a space so self._prev is not a
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# 1 letter word
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if self.is_final(self._prev):
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# case (1) [-2:not space][-1:final letter][cur:space]
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self._final_char_logical_score += 1
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elif self.is_non_final(self._prev):
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# case (2) [-2:not space][-1:Non-Final letter][
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# cur:space]
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self._final_char_visual_score += 1
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else:
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# Not standing on a space
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if ((self._before_prev == ' ') and
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(self.is_final(self._prev)) and (cur != ' ')):
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# case (3) [-2:space][-1:final letter][cur:not space]
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self._final_char_visual_score += 1
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self._before_prev = self._prev
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self._prev = cur
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# Forever detecting, till the end or until both model probers return
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# ProbingState.NOT_ME (handled above)
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return ProbingState.DETECTING
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@property
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def charset_name(self):
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# Make the decision: is it Logical or Visual?
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# If the final letter score distance is dominant enough, rely on it.
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finalsub = self._final_char_logical_score - self._final_char_visual_score
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if finalsub >= self.MIN_FINAL_CHAR_DISTANCE:
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return self.LOGICAL_HEBREW_NAME
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if finalsub <= -self.MIN_FINAL_CHAR_DISTANCE:
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return self.VISUAL_HEBREW_NAME
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# It's not dominant enough, try to rely on the model scores instead.
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modelsub = (self._logical_prober.get_confidence()
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- self._visual_prober.get_confidence())
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if modelsub > self.MIN_MODEL_DISTANCE:
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return self.LOGICAL_HEBREW_NAME
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if modelsub < -self.MIN_MODEL_DISTANCE:
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return self.VISUAL_HEBREW_NAME
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# Still no good, back to final letter distance, maybe it'll save the
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# day.
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if finalsub < 0.0:
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return self.VISUAL_HEBREW_NAME
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# (finalsub > 0 - Logical) or (don't know what to do) default to
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# Logical.
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return self.LOGICAL_HEBREW_NAME
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@property
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def language(self):
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return 'Hebrew'
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@property
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def state(self):
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# Remain active as long as any of the model probers are active.
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if (self._logical_prober.state == ProbingState.NOT_ME) and \
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(self._visual_prober.state == ProbingState.NOT_ME):
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return ProbingState.NOT_ME
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return ProbingState.DETECTING
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