Updated txt layer correctness classifier features

dedoc/download_models.py

@@ -11,7 +11,7 @@
 Keys are the names of repositories with models.
 """
 model_hash_dict = dict(
-    txtlayer_classifier="93b10fea2b661d7eca79381b47e5c4ebe2a22e75",
+    txtlayer_classifier="94e27e184fa2876883d260e0aa58b042e6ab3e35",
     scan_orientation_efficient_net_b0="0160965f8a920d12afacf62b8a5a8a3b365b11ef",
     font_classifier="db4481ad60ab050cbb42079b64f97f9e431feb07",
     paragraph_classifier="00bf989876cec171c1cf9859a6b712af6445e864",

dedoc/readers/pdf_reader/pdf_auto_reader/txtlayer_feature_extractor.py

@@ -8,17 +8,15 @@
 class TxtlayerFeatureExtractor:
 
     def __init__(self) -> None:
-        eng = "".join(list(map(chr, range(ord("a"), ord("z") + 1))))
-        rus = "".join([chr(i) for i in range(ord("а"), ord("а") + 32)] + ["ё"])
-
-        self.lower_letters = eng + rus
+        self.eng = "".join(list(map(chr, range(ord("a"), ord("z") + 1))))
+        self.rus = "".join([chr(i) for i in range(ord("а"), ord("а") + 32)] + ["ё"])
+        self.lower_letters = self.eng + self.rus
         self.upper_letters = self.lower_letters.upper()
         self.letters = self.upper_letters + self.lower_letters
         self.digits = "".join([str(i) for i in range(10)])
         self.special_symbols = "<>~!@#$%^&*_+-/\"|?.,:;'`= "
         self.brackets = "{}[]()"
         self.symbols = self.letters + self.digits + self.brackets + self.special_symbols
-        self.consonants = "".join(i for i in self.lower_letters if i not in "аоуыэяёюиеaeiouy")
 
         self.prohibited_symbols = {s: i for i, s in enumerate("[]<")}
 
@@ -30,13 +28,15 @@ def transform(self, texts: List[str]) -> pd.DataFrame:
             num_digits = self.__count_symbols(text, self.digits)
             num_special_symbols = self.__count_symbols(text, self.special_symbols)
             num_brackets = self.__count_symbols(text, self.brackets)
-            num_consonants = self.__count_symbols(text.lower(), self.consonants)
+            num_rus = self.__count_symbols(text, self.rus + self.rus.upper())
+            num_eng = self.__count_symbols(text, self.eng + self.eng.upper())
 
             features["letters_proportion"].append(num_letters / len(text))
             features["digits_proportion"].append(num_digits / len(text))
             features["special_symbols_proportion"].append(num_special_symbols / len(text))
             features["brackets_proportion"].append(num_brackets / len(text))
-            features["consonants_proportion"].append(num_consonants / num_letters if num_letters != 0 else 0.0)
+            features["rus_proportion"].append(num_rus / len(text))
+            features["eng_proportion"].append(num_eng / len(text))
 
             for symbol in self.letters + self.digits:
                 n = num_letters + num_digits
@@ -59,6 +59,15 @@ def transform(self, texts: List[str]) -> pd.DataFrame:
             features["letter_changes"].append(letter_changes / len(text))
 
             features["mean_word_length"].append(np.mean([len(word) for word in text.split()]))
+            features["median_word_length"].append(np.median([len(word) for word in text.split()]))
+
+            all_characters_ord = [ord(character) for character in text]
+            trash_chars = sum(1 for s in all_characters_ord if s <= 32 or 160 <= s <= 879)
+            features["trash_chars_proportion"].append(trash_chars / len(text))
+            features["trash_chars_number"].append(trash_chars)
+            features["std_char_ord"].append(np.std(all_characters_ord))
+            features["mean_char_ord"].append(np.mean(all_characters_ord))
+            features["median_char_ord"].append(np.median(all_characters_ord))
         features = pd.DataFrame(features)
         return features[sorted(features.columns)].astype(float)
 

resources/benchmarks/benchmarks_tl_correctness.json

@@ -1,24 +1,21 @@
 {
-    "version": "0.9.2",
+    "version": "0.11.2",
     "guessing_the_correctness_of_the_text": {
-        "percentage_of_guessed_correct_tl": 0.9699570815450643,
+        "percentage_of_guessed_correct_tl": 0.9785407725321889,
         "list_of_file_with_incorrect_tl": [
-            "access-the-vision-for-2013.pdf",
+            "hogans-federal-motion-for-a-preliminary-injunction_1616093696_24.pdf",
             "afcea-spy.pdf",
             "b96a__usmc-combat-camera-directory.pdf",
-            "HBG-JMP-CIM_1616126784_120.pdf",
-            "demystifying-nge-rock-ridge_1643518222_537.pdf",
-            "nifog-2009_1616541890_542.pdf",
-            "hogans-federal-motion-for-a-preliminary-injunction_1616093696_24.pdf"
+            "access-the-vision-for-2013.pdf",
+            "demystifying-nge-rock-ridge_1643518222_537.pdf"
         ],
-        "percentage_of_guessed_incorrect_tl": 0.75,
+        "percentage_of_guessed_incorrect_tl": 0.7916666666666666,
         "list_of_file_with_correct_tl": [
-            "slides.pdf",
-            "ЧММФ_Абакумов_учебник.pdf",
             "PE20_1616439522_1.pdf",
+            "slides.pdf",
+            "PE157_1616278053_181.pdf",
             "EXTERNAL FORMS - SUPPORTING DOCUMENTATION-ESHS9615401 2017_07_27 11_22_39_1616049888_455.pdf",
-            "cu-spy-holes_1616346633_620.pdf",
-            "PE157_1616278053_181.pdf"
+            "ЧММФ_Абакумов_учебник.pdf"
         ]
     }
 }