ÿØÿà JFIF    ÿÛ „  ( %"1!%)+...383,7(-.+  -+++--++++---+-+-----+---------------+---+-++7-----ÿÀ  ß â" ÿÄ     ÿÄ H    !1AQaq"‘¡2B±ÁÑð#R“Ò Tbr‚²á3csƒ’ÂñDS¢³$CÿÄ   ÿÄ %  !1AQa"23‘ÿÚ   ? ôÿ ¨pŸªáÿ —åYõõ\?àÒü©ŠÄï¨pŸªáÿ —åYõõ\?àÓü©ŠÄá 0Ÿªáÿ Ÿå[úƒ ú®ði~TÁbqÐ8OÕpÿ ƒOò¤Oè`–RÂáœá™êi€ßÉ< FtŸI“öÌ8úDf´°å}“¾œ6  öFá°y¥jñÇh†ˆ¢ã/ÃÐ:ªcÈ "Y¡ðÑl>ÿ ”ÏËte:qž\oäŠe÷󲍷˜HT4&ÿ ÓÐü6ö®¿øþßèô Ÿ•7Ñi’•j|“ñì>b…þS?*Óôÿ ÓÐü*h¥£ír¶ü UãSç‚Ÿ[AÐaè[ûª•õ&õj?†Éö+EzP—WeÒírJFt ‘BŒ†Ï‡%#tE Øz ¥OÛ«!1›üä±Í™%ºÍãö]°î(–:@<‹ŒÊö×òÆt¦ãº+‡¦%ÌÁ²h´OƒJŒtMÜ>ÀÜÊw3Y´•牋4ǍýʏTì>œú=Íwhyë,¾Ôò×õ¿ßÊa»«þˆѪQ|%6ž™A õ%:øj<>É—ÿ Å_ˆCbõ¥š±ý¯Ýƒï…¶|RëócÍf溪“t.СøTÿ *Ä¿-{†çàczůŽ_–^XþŒ±miB[X±d 1,é”zEù»& î9gœf™9Ð'.;—™i}!ôšåîqêÛ٤ёý£½ÆA–àôe"A$˝Úsäÿ ÷Û #°xŸëí(l »ý3—¥5m! rt`†0~'j2(]S¦¦kv,ÚÇ l¦øJA£Šƒ J3E8ÙiŽ:cÉžúeZ°€¯\®kÖ(79«Ž:¯X”¾³Š&¡* ….‰Ž(ÜíŸ2¥ª‡×Hi²TF¤ò[¨íÈRëÉ䢍mgÑ.Ÿ<öäS0í„ǹÁU´f#Vß;Õ–…P@3ío<ä-±»Ž.L|kªÀê›fÂ6@»eu‚|ÓaÞÆŸ…¨ááå>åŠ?cKü6ùTÍÆ”†sĤÚ;H2RÚ†õ\Ö·Ÿn'¾ ñ#ºI¤Å´%çÁ­‚â7›‹qT3Iï¨ÖÚ5I7Ë!ÅOóŸ¶øÝñØôת¦$Tcö‘[«Ö³šÒ';Aþ ¸èíg A2Z"i¸vdÄ÷.iõ®§)¿]¤À†–‡É&ä{V¶iŽ”.Ó×Õÿ û?h¬Mt–íª[ÿ Ñÿ ÌV(í}=ibÔ¡›¥¢±b Lô¥‡piη_Z<‡z§èŒ)iÖwiÇ 2hÙ3·=’d÷8éŽ1¦¸c¤µ€7›7Ø ð\á)} ¹fËí›pAÃL%âc2 í§æQz¿;T8sæ°qø)QFMð‰XŒÂ±N¢aF¨…8¯!U  Z©RÊ ÖPVÄÀÍin™Ì-GˆªÅËŠ›•zË}º±ŽÍFò¹}Uw×#ä5B¤{î}Ð<ÙD é©¤&‡ïDbàÁôMÁ." ¤‡ú*õ'VŽ|¼´Úgllº¼klz[Æüï÷Aób‡Eÿ dÑ»Xx9ÃÜ£ÁT/`¼¸vI±Ýµ·Ë‚“G³þ*Ÿû´r|*}<¨îºœ @¦mÄ’M¹”.œ«Y–|6ÏU¤jç¥ÕÞqO ˜kDÆÁ¨5ÿ š;Њ¦¦€GÙk \ –Þ=â¼=SͧµªS°ÚÍpÜãQűÀõ¬?ÃÁ1Ñ•õZà?hóœ€ L¦l{Y*K˜Ù›zc˜–ˆâ ø+¾ ­-Ök¥%ùEÜA'}ˆ><ÊIè“bpÍ/qÞâvoX€w,\úªò6Z[XdÒæ­@Ö—€$òJí#é>'°Ú ôª˜<)4ryÙ£|óAÅn5žêŸyÒäMÝ2{"}‰–¤l÷ûWX\l¾Á¸góÉOÔ /óñB¤f¸çñ[.P˜ZsÊË*ßT܈§QN¢’¡¨§V¼(Üù*eÕ“”5T¨‹Âê¥FŒã½Dü[8'Ò¥a…Ú¶k7a *•›¼'Ò·\8¨ª\@\õ¢¦íq+DÙrmÎ…_ªæ»ŠÓœ¡¯’Ré9MÅ×D™lælffc+ŒÑ,ý™ÿ ¯þǤ=Å’Á7µ÷ÚÛ/“Ü€ñýã¼àí¾ÕÑ+ƒ,uµMâÀÄbm:ÒÎPæ{˜Gz[ƒ¯«® KHà`ßØŠéí¯P8Aq.C‰ à€kòpj´kN¶qô€…Õ,ÜNŠª-­{Zö’æû44‰sŽè‰îVíRœÕm" 6?³D9¡ÇTíÅꋇ`4«¸ÝÁô ï’ýorqКÇZ«x4Žâéþuïf¹µö[P ,Q£éaX±`PÉÍZ ¸äYúg üAx ’6Lê‚xÝÓ*äQ  Ï’¨hÍ =²,6ï#rÃ<¯–£»ƒ‹,–ê•€ aÛsñ'%Æ"®ÛüìBᝠHÚ3ß°©$“XnœÖ’î2ËTeûìxîß ¦å¿çÉ ðK§þ{‘t‚Ϋ¬jéîZ[ ”š7L¥4VÚCE×]m¤Øy”ä4-dz£œ§¸x.*ãÊÊ b÷•h:©‡¦s`BTÁRû¾g⻩‹jø sF¢àJøFl‘È•Xᓁà~*j¯ +(ÚÕ6-£¯÷GŠØy‚<Ç’.F‹Hœw(+)ÜÜâÈzÄäT§FߘãÏ;DmVœ3Àu@mÚüXÝü•3B¨òÌÁÛ<·ÃÜ z,Ì@õÅ·d2]ü8s÷IôÞ¯^Ç9¢u„~ëAŸï4«M? 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All Rights Reserved. # # Contributor(s): # Mark Pilgrim - port to Python # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA # 02110-1301 USA ######################### END LICENSE BLOCK ######################### from .charsetprober import CharSetProber from .enums import ProbingState # This prober doesn't actually recognize a language or a charset. # It is a helper prober for the use of the Hebrew model probers ### General ideas of the Hebrew charset recognition ### # # Four main charsets exist in Hebrew: # "ISO-8859-8" - Visual Hebrew # "windows-1255" - Logical Hebrew # "ISO-8859-8-I" - Logical Hebrew # "x-mac-hebrew" - ?? Logical Hebrew ?? # # Both "ISO" charsets use a completely identical set of code points, whereas # "windows-1255" and "x-mac-hebrew" are two different proper supersets of # these code points. windows-1255 defines additional characters in the range # 0x80-0x9F as some misc punctuation marks as well as some Hebrew-specific # diacritics and additional 'Yiddish' ligature letters in the range 0xc0-0xd6. # x-mac-hebrew defines similar additional code points but with a different # mapping. # # As far as an average Hebrew text with no diacritics is concerned, all four # charsets are identical with respect to code points. Meaning that for the # main Hebrew alphabet, all four map the same values to all 27 Hebrew letters # (including final letters). # # The dominant difference between these charsets is their directionality. # "Visual" directionality means that the text is ordered as if the renderer is # not aware of a BIDI rendering algorithm. The renderer sees the text and # draws it from left to right. The text itself when ordered naturally is read # backwards. A buffer of Visual Hebrew generally looks like so: # "[last word of first line spelled backwards] [whole line ordered backwards # and spelled backwards] [first word of first line spelled backwards] # [end of line] [last word of second line] ... etc' " # adding punctuation marks, numbers and English text to visual text is # naturally also "visual" and from left to right. # # "Logical" directionality means the text is ordered "naturally" according to # the order it is read. It is the responsibility of the renderer to display # the text from right to left. A BIDI algorithm is used to place general # punctuation marks, numbers and English text in the text. # # Texts in x-mac-hebrew are almost impossible to find on the Internet. From # what little evidence I could find, it seems that its general directionality # is Logical. # # To sum up all of the above, the Hebrew probing mechanism knows about two # charsets: # Visual Hebrew - "ISO-8859-8" - backwards text - Words and sentences are # backwards while line order is natural. For charset recognition purposes # the line order is unimportant (In fact, for this implementation, even # word order is unimportant). # Logical Hebrew - "windows-1255" - normal, naturally ordered text. # # "ISO-8859-8-I" is a subset of windows-1255 and doesn't need to be # specifically identified. # "x-mac-hebrew" is also identified as windows-1255. A text in x-mac-hebrew # that contain special punctuation marks or diacritics is displayed with # some unconverted characters showing as question marks. This problem might # be corrected using another model prober for x-mac-hebrew. Due to the fact # that x-mac-hebrew texts are so rare, writing another model prober isn't # worth the effort and performance hit. # #### The Prober #### # # The prober is divided between two SBCharSetProbers and a HebrewProber, # all of which are managed, created, fed data, inquired and deleted by the # SBCSGroupProber. The two SBCharSetProbers identify that the text is in # fact some kind of Hebrew, Logical or Visual. The final decision about which # one is it is made by the HebrewProber by combining final-letter scores # with the scores of the two SBCharSetProbers to produce a final answer. # # The SBCSGroupProber is responsible for stripping the original text of HTML # tags, English characters, numbers, low-ASCII punctuation characters, spaces # and new lines. It reduces any sequence of such characters to a single space. # The buffer fed to each prober in the SBCS group prober is pure text in # high-ASCII. # The two SBCharSetProbers (model probers) share the same language model: # Win1255Model. # The first SBCharSetProber uses the model normally as any other # SBCharSetProber does, to recognize windows-1255, upon which this model was # built. The second SBCharSetProber is told to make the pair-of-letter # lookup in the language model backwards. This in practice exactly simulates # a visual Hebrew model using the windows-1255 logical Hebrew model. # # The HebrewProber is not using any language model. All it does is look for # final-letter evidence suggesting the text is either logical Hebrew or visual # Hebrew. Disjointed from the model probers, the results of the HebrewProber # alone are meaningless. HebrewProber always returns 0.00 as confidence # since it never identifies a charset by itself. Instead, the pointer to the # HebrewProber is passed to the model probers as a helper "Name Prober". # When the Group prober receives a positive identification from any prober, # it asks for the name of the charset identified. If the prober queried is a # Hebrew model prober, the model prober forwards the call to the # HebrewProber to make the final decision. In the HebrewProber, the # decision is made according to the final-letters scores maintained and Both # model probers scores. The answer is returned in the form of the name of the # charset identified, either "windows-1255" or "ISO-8859-8". class HebrewProber(CharSetProber): # windows-1255 / ISO-8859-8 code points of interest FINAL_KAF = 0xea NORMAL_KAF = 0xeb FINAL_MEM = 0xed NORMAL_MEM = 0xee FINAL_NUN = 0xef NORMAL_NUN = 0xf0 FINAL_PE = 0xf3 NORMAL_PE = 0xf4 FINAL_TSADI = 0xf5 NORMAL_TSADI = 0xf6 # Minimum Visual vs Logical final letter score difference. # If the difference is below this, don't rely solely on the final letter score # distance. MIN_FINAL_CHAR_DISTANCE = 5 # Minimum Visual vs Logical model score difference. # If the difference is below this, don't rely at all on the model score # distance. MIN_MODEL_DISTANCE = 0.01 VISUAL_HEBREW_NAME = "ISO-8859-8" LOGICAL_HEBREW_NAME = "windows-1255" def __init__(self): super(HebrewProber, self).__init__() self._final_char_logical_score = None self._final_char_visual_score = None self._prev = None self._before_prev = None self._logical_prober = None self._visual_prober = None self.reset() def reset(self): self._final_char_logical_score = 0 self._final_char_visual_score = 0 # The two last characters seen in the previous buffer, # mPrev and mBeforePrev are initialized to space in order to simulate # a word delimiter at the beginning of the data self._prev = ' ' self._before_prev = ' ' # These probers are owned by the group prober. def set_model_probers(self, logicalProber, visualProber): self._logical_prober = logicalProber self._visual_prober = visualProber def is_final(self, c): return c in [self.FINAL_KAF, self.FINAL_MEM, self.FINAL_NUN, self.FINAL_PE, self.FINAL_TSADI] def is_non_final(self, c): # The normal Tsadi is not a good Non-Final letter due to words like # 'lechotet' (to chat) containing an apostrophe after the tsadi. This # apostrophe is converted to a space in FilterWithoutEnglishLetters # causing the Non-Final tsadi to appear at an end of a word even # though this is not the case in the original text. # The letters Pe and Kaf rarely display a related behavior of not being # a good Non-Final letter. Words like 'Pop', 'Winamp' and 'Mubarak' # for example legally end with a Non-Final Pe or Kaf. However, the # benefit of these letters as Non-Final letters outweighs the damage # since these words are quite rare. return c in [self.NORMAL_KAF, self.NORMAL_MEM, self.NORMAL_NUN, self.NORMAL_PE] def feed(self, byte_str): # Final letter analysis for logical-visual decision. # Look for evidence that the received buffer is either logical Hebrew # or visual Hebrew. # The following cases are checked: # 1) A word longer than 1 letter, ending with a final letter. This is # an indication that the text is laid out "naturally" since the # final letter really appears at the end. +1 for logical score. # 2) A word longer than 1 letter, ending with a Non-Final letter. In # normal Hebrew, words ending with Kaf, Mem, Nun, Pe or Tsadi, # should not end with the Non-Final form of that letter. Exceptions # to this rule are mentioned above in isNonFinal(). This is an # indication that the text is laid out backwards. +1 for visual # score # 3) A word longer than 1 letter, starting with a final letter. Final # letters should not appear at the beginning of a word. This is an # indication that the text is laid out backwards. +1 for visual # score. # # The visual score and logical score are accumulated throughout the # text and are finally checked against each other in GetCharSetName(). # No checking for final letters in the middle of words is done since # that case is not an indication for either Logical or Visual text. # # We automatically filter out all 7-bit characters (replace them with # spaces) so the word boundary detection works properly. [MAP] if self.state == ProbingState.NOT_ME: # Both model probers say it's not them. No reason to continue. return ProbingState.NOT_ME byte_str = self.filter_high_byte_only(byte_str) for cur in byte_str: if cur == ' ': # We stand on a space - a word just ended if self._before_prev != ' ': # next-to-last char was not a space so self._prev is not a # 1 letter word if self.is_final(self._prev): # case (1) [-2:not space][-1:final letter][cur:space] self._final_char_logical_score += 1 elif self.is_non_final(self._prev): # case (2) [-2:not space][-1:Non-Final letter][ # cur:space] self._final_char_visual_score += 1 else: # Not standing on a space if ((self._before_prev == ' ') and (self.is_final(self._prev)) and (cur != ' ')): # case (3) [-2:space][-1:final letter][cur:not space] self._final_char_visual_score += 1 self._before_prev = self._prev self._prev = cur # Forever detecting, till the end or until both model probers return # ProbingState.NOT_ME (handled above) return ProbingState.DETECTING @property def charset_name(self): # Make the decision: is it Logical or Visual? # If the final letter score distance is dominant enough, rely on it. finalsub = self._final_char_logical_score - self._final_char_visual_score if finalsub >= self.MIN_FINAL_CHAR_DISTANCE: return self.LOGICAL_HEBREW_NAME if finalsub <= -self.MIN_FINAL_CHAR_DISTANCE: return self.VISUAL_HEBREW_NAME # It's not dominant enough, try to rely on the model scores instead. modelsub = (self._logical_prober.get_confidence() - self._visual_prober.get_confidence()) if modelsub > self.MIN_MODEL_DISTANCE: return self.LOGICAL_HEBREW_NAME if modelsub < -self.MIN_MODEL_DISTANCE: return self.VISUAL_HEBREW_NAME # Still no good, back to final letter distance, maybe it'll save the # day. if finalsub < 0.0: return self.VISUAL_HEBREW_NAME # (finalsub > 0 - Logical) or (don't know what to do) default to # Logical. return self.LOGICAL_HEBREW_NAME @property def language(self): return 'Hebrew' @property def state(self): # Remain active as long as any of the model probers are active. if (self._logical_prober.state == ProbingState.NOT_ME) and \ (self._visual_prober.state == ProbingState.NOT_ME): return ProbingState.NOT_ME return ProbingState.DETECTING