editing the readme

README.md

@@ -1,3 +1,61 @@
 ---
 license: cc0-1.0
+task_categories:
+- image-to-text
+- optical-character-recognition
+language:
+- he
+tags:
+- biblical-text
+- hebrew
+- new-testament
+- ocr
+- text-recognition
+- religious-text
+size_categories:
+- 1K-10K
 ---
+
+# Dataset Card for Hebrew Biblical Text Images
+
+## Dataset Description
+
+### Dataset Summary
+
+This dataset contains rendered images of biblical texts in Hebrew, specifically covering books from the Elias Hutter's Besorah (New Testament). The dataset is organized into two main subsets:
+
+- **Hebrew** (`hebrew/`): Processed/rendered Hebrew text images (1,243 images)
+- **Raw** (`raw/`): Original raw text images (2,541 images)
+
+The dataset covers 27 books of the New Testament including Acts, Colossians, Corinthians (1 & 2), Ephesians, Galatians, Hebrews, James, John (1, 2, 3), Jude, Luke, Mark, Matthew, Peter (1 & 2), Philemon, Philippians, Revelation, Romans, Thessalonians (1 & 2), Timothy (1 & 2), Titus, and others.
+
+Each book is organized in its own directory, with images sequentially numbered (e.g., `000001.png`, `000002.png`).
+
+### Supported Tasks and Leaderboards
+
+This dataset is designed for:
+
+- **Optical Character Recognition (OCR)**: Training and evaluating OCR models to recognize Hebrew text from rendered images
+- **Image-to-Text**: Converting rendered text images back to machine-readable Hebrew text
+- **Biblical Text Processing**: Research and analysis of biblical texts in Hebrew
+- **Document Understanding**: Understanding document structure and layout of religious texts
+- **Multilingual OCR**: Supporting Hebrew language OCR research
+
+
+### Citation Information
+
+If you use this dataset in your research, please cite it as:
+
+```bibtex
+@dataset{hebrew_biblical_text_images,
+  title={Hebrew Biblical Text Images Dataset},
+  author={[Your Name]},
+  year={2025},
+  url={https://huggingface.co/datasets/[username]/[dataset-name]},
+  license={CC0-1.0}
+}
+```
+
+### Contributions
+
+Contributions, suggestions, and improvements are welcome. Please open an issue or submit a pull request if you have suggestions for improving the dataset or documentation.