# rupindersingh1313/30_8_2025_dataset

## Dataset Description

This dataset contains Punjabi OCR data with page images and their corresponding text annotations, ready for machine learning applications.

### Dataset Summary

- **Language**: Punjabi (pa-IN)
- **Script**: Gurmukhi
- **Total Pages**: 769
- **Source**: Generated using Punjabi OCR annotation pipeline
- **Format**: Image-annotation pairs with original JSON annotations

### Dataset Splits

- **Train**: 615 samples
- **Validation**: 76 samples
- **Test**: 78 samples

The dataset is split into train/validation/test sets with an 80/10/10 ratio by default:
- Training set for model training
- Validation set for hyperparameter tuning and model selection
- Test set for final evaluation

### Dataset Structure

Each row contains:
- `image`: The page image (PNG format, high resolution)
- `annotation`: Complete OCR annotation in JSON format (as string)

The annotation JSON contains the original structure with:
- Document metadata (language, script, image dimensions)
- Text hierarchy (regions, lines, words)
- Bounding box coordinates for all text elements
- Complete text transcription

### Usage

```python
from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("rupindersingh1313/30_8_2025_dataset")

# Access different splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]

# Iterate through training data
for sample in train_data:
    image = sample["image"]
    annotation = json.loads(sample["annotation"])  # Parse JSON annotation
    print(f"Image shape: {image.size}")
    print(f"Annotation keys: {list(annotation.keys())}")
```

### Annotation Format

The annotation field contains JSON with this structure:
```json
{
  "document": {
    "id": "doc_001",
    "language": "pa-IN",
    "script": "Gurmukhi",
    "image": {"width": 2481, "height": 3507, "dpi": 300}
  },
  "hierarchy": {
    "regions": [
      {
        "region_id": 1,
        "type": "text_block",
        "polygon": [x1, y1, x2, y2, ...],
        "lines": [
          {
            "line_id": 1,
            "polygon": [x1, y1, x2, y2, ...],
            "words": [
              {
                "word_id": 1,
                "text": "ਪੰਜਾਬੀ",
                "polygon": [x1, y1, x2, y2, ...]
              }
            ]
          }
        ]
      }
    ]
  }
}
```

### Use Cases

This dataset is suitable for:
- **OCR Model Training**: Train custom OCR models for Punjabi text
- **Text Detection**: Develop text region detection algorithms
- **Document Layout Analysis**: Analyze document structure and layout
- **Multilingual NLP**: Include Punjabi in multilingual language models
- **Research**: Academic research in OCR and document processing

### Data Quality

- High-resolution images (300 DPI)
- Accurate text transcriptions
- Precise bounding box annotations
- Consistent formatting and structure
- Quality-controlled annotation process

### License

Please ensure proper attribution when using this dataset. Contact the dataset creators for commercial use permissions.

### Citation

If you use this dataset, please cite:

```bibtex
@dataset{punjabi_ocr_dataset,
  title={Punjabi OCR Dataset - rupindersingh1313/30_8_2025_dataset},
  author={Generated using Punjabi OCR Pipeline},
  year={2025},
  url={https://huggingface.co/datasets/rupindersingh1313/30_8_2025_dataset},
  note={High-quality Punjabi OCR dataset with images and annotations}
}
```

### Contact

For questions, issues, or contributions, please contact the dataset maintainers.