updated script

save_parquet.py

@@ -14,6 +14,16 @@ from tqdm import tqdm
 from sklearn.model_selection import train_test_split
 import multiprocessing
 from concurrent.futures import ProcessPoolExecutor
+import re
+
+arabic_pattern = re.compile(r"[\u0600-\u06FF\u0750-\u077F\u08A0-\u08FF]")
+english_pattern = re.compile(r"[A-Za-z]")
+
+
+def contains_english_and_arabic(line: str) -> bool:
+    has_arabic = bool(arabic_pattern.search(line))
+    has_english = bool(english_pattern.search(line))
+    return has_arabic and has_english
 
 
 class ArabicOCRDatasetConverter:
@@ -47,8 +57,14 @@ class ArabicOCRDatasetConverter:
                     parts = line.split(",")
                     if len(parts) < 2:
                         parts = line.split("\t")
+                    if len(parts) < 2:
+                        self.logger.warning(f"Skipping invalid line: {line}")
+                        continue
                     if "/" in parts[0]:
                         parts[0] = parts[0].split("/")[-1]
+                    if contains_english_and_arabic(parts[1]):
+                        self.logger.warning(f"Skipping mixed language line: {parts[1]}")
+                        continue
                     annotations[parts[0].strip()] = parts[1].strip()
         except Exception as e:
             self.logger.error(f"Error parsing {annotation_path}: {e}")
@@ -247,7 +263,9 @@ def generate_synthetic_bigrams():
     print(f"Train Bigram Synthetic shape: {final_train.shape}")
 
 
-def process_ktsrv_dataset(dataset_root: str, output_dir: str = "ktsrv_parquets"):
+def process_ktsrv_dataset(
+    dataset_root: str, output_dir: str = "ktsrv_parquets", prefix="ktsrv"
+):
     root = Path(dataset_root)
     out_dir = Path(output_dir)
     out_dir.mkdir(parents=True, exist_ok=True)
@@ -266,17 +284,25 @@ def process_ktsrv_dataset(dataset_root: str, output_dir: str = "ktsrv_parquets")
             print(f"Skipping {split_type}-{lang}, no gt.txt found")
             continue
 
-        converter = ArabicOCRDatasetConverter(dataset_dir=str(folder) + "/" + lang)
-        parquet_name = f"ktsrv_{split_type}_{lang}.parquet"
-        parquet_path = out_dir / parquet_name
-        df = converter.save_parquet(
-            output_path=str(parquet_path),
-            annotation_path=str(gt_file),
-            include_images=True,
+        parquet_path, df = convert_folder_to_parquet(
+            prefix, out_dir, split_type + "_" + lang, str(folder) + "/" + lang, gt_file
         )
         print(f"Saved {parquet_path} with {len(df)} samples")
 
 
+def convert_folder_to_parquet(prefix, out_dir, suffix, folder, gt_file):
+    converter = ArabicOCRDatasetConverter(dataset_dir=folder)
+    parquet_name = f"{prefix}_{suffix}.parquet"
+    parquet_path = out_dir + "/" + parquet_name
+    df = converter.save_parquet(
+        output_path=str(parquet_path),
+        annotation_path=str(gt_file),
+        include_images=True,
+    )
+
+    return parquet_path, df
+
+
 if __name__ == "__main__":
     # final_train_df, final_test_df = process_and_combine_datasets()
     # generate_synthetic_bigrams()