| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: source | |
| dtype: string | |
| - name: text | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 4555446.0 | |
| num_examples: 800 | |
| download_size: 4393010 | |
| dataset_size: 4555446.0 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: apache-2.0 | |
| task_categories: | |
| - image-to-text | |
| tags: | |
| - arabic | |
| - ocr | |
| - document-understanding | |
| - multi-modal | |
| KITAB-Bench is a comprehensive benchmark for Arabic OCR and document understanding, evaluating performance across multiple domains and tasks, including text recognition, layout detection, table and chart extraction, and more. It includes 8,809 samples across 9 major domains and features high-quality human-labeled annotations. This dataset provides image data, source information, and corresponding text transcriptions. | |
| **Please see paper & code for more information:** | |
| - [Project Page](https://mbzuai-oryx.github.io/KITAB-Bench/) | |
| - [GitHub Repository](https://github.com/mbzuai-oryx/KITAB-Bench) | |
| - [Arxiv Paper](https://arxiv.org/abs/2502.14949) |