| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: text | |
| dtype: string | |
| - name: source | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 13421862.0 | |
| num_examples: 200 | |
| download_size: 13211013 | |
| dataset_size: 13421862.0 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: apache-2.0 | |
| task_categories: | |
| - image-to-text | |
| KITAB-Bench is a comprehensive multi-domain benchmark for Arabic OCR and document understanding. It evaluates the performance of traditional OCR, vision-language models (VLMs), and specialized AI systems on diverse document types including PDFs, handwritten text, structured tables, financial & legal reports, and more. The benchmark includes nine major domains across 8,809 samples and offers novel evaluation metrics such as Markdown Recognition Score (MARS), Table Edit Distance Score (TEDS), and Chart Representation Metric (SCRM). | |
| **Key Features:** | |
| * 9 major domains & 36 sub-domains | |
| * Diverse document types: PDFs, handwritten text, structured tables, financial & legal reports | |
| * Strong baselines: Benchmarked against Tesseract, GPT-4o, Gemini, Qwen, and more | |
| * Novel evaluation metrics: MARS, TEDS, SCRM, and more | |
| **Please see paper & code for more information:** | |
| - [Paper](https://arxiv.org/abs/2502.14949) | |
| - [Project Page](https://mbzuai-oryx.github.io/KITAB-Bench/) | |
| - [Github Repository](https://github.com/mbzuai-oryx/KITAB-Bench) |