""" Upload Odia OCR Benchmark Dataset to HuggingFace Hub Converts local images + metadata.csv to HuggingFace Dataset format and pushes. """ import argparse from pathlib import Path from datasets import Dataset, Features, Value, Image from huggingface_hub import HfApi import pandas as pd BENCHMARK_DIR = Path(__file__).parent.parent / "benchmark_dataset" CSV_PATH = BENCHMARK_DIR / "final_hf.csv" def resolve_image_path(raw_path: str) -> Path: """Resolve image paths from metadata across common path styles.""" p = Path(str(raw_path).strip()) # 1) Already absolute if p.is_absolute(): return p # 2) Relative to project root: benchmark_dataset/images/... if p.parts and p.parts[0] == "benchmark_dataset": return (BENCHMARK_DIR.parent / p).resolve() # 3) Relative to benchmark dir: images/... return (BENCHMARK_DIR / p).resolve() def load_local_dataset() -> Dataset: """Load local images and metadata into a HuggingFace Dataset.""" print(f"Loading metadata from {CSV_PATH}...") if not CSV_PATH.exists(): raise FileNotFoundError(f"Metadata CSV not found: {CSV_PATH}") df = pd.read_csv(CSV_PATH) print(f"Found {len(df)} samples in metadata") # Normalize image paths, supporting: # - images/... # - benchmark_dataset/images/... # - absolute paths df["image_path"] = df["image_path"].apply(lambda p: str(resolve_image_path(p))) # Verify images exist missing = [] for idx, row in df.iterrows(): if not Path(row["image_path"]).exists(): missing.append(row["image_path"]) if missing: print(f"Warning: {len(missing)} images not found:") for p in missing[:5]: print(f" - {p}") if len(missing) > 5: print(f" ... and {len(missing) - 5} more") # Filter out missing images df = df[df["image_path"].apply(lambda p: Path(p).exists())] print(f"Continuing with {len(df)} valid samples") if "id" not in df.columns: raise ValueError( f"Required column 'id' not found in {CSV_PATH}. " "Please add an 'id' column before upload." ) # Create dataset with Image feature features = Features({ "id": Value("int64"), "image": Image(), "ground_truth": Value("string"), "category": Value("string"), }) # Rename image_path to image for HF Dataset data = { "id": df["id"].tolist(), "image": df["image_path"].tolist(), "ground_truth": df["ground_truth"].tolist(), "category": df["category"].tolist(), } dataset = Dataset.from_dict(data, features=features) print(f"Created HuggingFace Dataset with {len(dataset)} samples") return dataset def push_to_hub(dataset: Dataset, repo_id: str, private: bool = False): """Push dataset to HuggingFace Hub.""" print(f"\nPushing to HuggingFace Hub: {repo_id}") print(f"Private: {private}") dataset.push_to_hub( repo_id, private=private, commit_message="Upload Odia OCR benchmark dataset", ) print(f"\nDataset uploaded to: https://huggingface.co/datasets/{repo_id}") def push_dataset_card(repo_id: str, card_content: str): """Upload dataset card as README.md to HuggingFace Hub.""" api = HfApi() api.upload_file( path_or_fileobj=card_content.encode("utf-8"), path_in_repo="README.md", repo_id=repo_id, repo_type="dataset", commit_message="Add dataset card README", ) print(f"Dataset card uploaded: https://huggingface.co/datasets/{repo_id}/blob/main/README.md") def create_dataset_card(repo_id: str): """Create a dataset card (README.md) for HuggingFace.""" card_content = f"""--- license: cc-by-4.0 task_categories: - image-to-text language: - or tags: - ocr - odia - oriya - indic - benchmark size_categories: - n<1K --- # Odia OCR Benchmark Dataset ## Description A curated benchmark dataset for evaluating OCR models on Odia (Oriya) text recognition. Contains handwritten, printed, scene text, newspaper, books, and digital categories, including both short samples and long-text examples for OCR evaluation. ## Dataset Structure - **id**: Unique identifier for each sample - **image**: The input image (PIL Image) - **ground_truth**: The correct Odia text transcription - **category**: Type of text (handwritten, printed, scene_text, newspaper, books, digital) ## Usage ```python from datasets import load_dataset dataset = load_dataset("{repo_id}") # Access a sample sample = dataset["train"][0] sample_id = sample["id"] image = sample["image"] text = sample["ground_truth"] ``` ## Categories | Category | Description | | ------------- | ----------------------------------------------------- | | handwritten | Handwritten Odia text (word/short phrase level) | | printed | Printed/typed Odia text | | scene_text | Text in natural scenes (signboards, posters, etc.) | | newspaper | Odia newspaper clippings (including long text) | | books | Scanned Odia book pages (including long text) | | digital | Screenshots from Odia digital content | ## Sources - `OdiaGenAIOCR/odia-ocr-merged` (handwritten) - `darknight054/indic-mozhi-ocr` with config `oriya` (printed) - `darknight054/indicstr12-crops` with config `odia` (scene_text) - `newspaper`: Odia newspaper scans/clippings - `books`: Odia book page images - `digital`: odia digital content ## Notes - Includes long-text samples for paragraph-level OCR evaluation. - The `source` field records origin for each sample. ## License CC-BY-4.0 """ return card_content def main(): parser = argparse.ArgumentParser( description="Upload Odia OCR benchmark dataset to HuggingFace Hub" ) parser.add_argument( "--repo", type=str, required=True, help="HuggingFace repo ID (e.g., 'username/odia-ocr-benchmark')", ) parser.add_argument( "--private", action="store_true", help="Make the dataset private", ) parser.add_argument( "--dry-run", action="store_true", help="Load and validate dataset without uploading", ) args = parser.parse_args() print("=" * 60) print("Upload Odia OCR Benchmark to HuggingFace") print("=" * 60) # Load local dataset dataset = load_local_dataset() # Show sample print("\nSample from dataset:") sample = dataset[0] print(f" id: {sample['id']}") print(f" ground_truth: {sample['ground_truth']}") print(f" category: {sample['category']}") if args.dry_run: print("\n[DRY RUN] Dataset validated. Not uploading.") return # Push to hub push_to_hub(dataset, args.repo, private=args.private) # Push dataset card card_content = create_dataset_card(args.repo) push_dataset_card(args.repo, card_content) if __name__ == "__main__": main()