---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: data
    dtype: string
  - name: uid
    dtype: int64
  splits:
  - name: train
    num_bytes: 99059837.0
    num_examples: 378
  download_size: 94572845
  dataset_size: 99059837.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- image-to-text
license: apache-2.0
tags:
  - arabic
  - ocr
  - document-understanding
  - multi-domain
  - benchmark
---

# KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding

KITAB-Bench is a comprehensive benchmark for evaluating Arabic OCR and document understanding. It covers 9 key tasks across 9 major domains and 36 sub-domains, including text recognition (OCR), layout detection, table recognition, PDF-to-Markdown conversion, chart-to-DataFrame extraction, diagram-to-JSON extraction, and Visual Question Answering (VQA).  The dataset includes over 8,809 samples with high-quality human-labeled annotations.  Novel evaluation metrics are introduced to accurately assess performance across these diverse tasks.

**Key Features:**

- 9 major domains & 36 sub-domains across 8,809 samples.
- Diverse document types: PDFs, handwritten text, structured tables, financial & legal reports.
- Evaluation across OCR, layout detection, table recognition, chart extraction, & PDF conversion.
- Novel evaluation metrics: MARS, TEDS, SCRM, CODM.


**For more details, including the complete methodology, evaluation metrics, and results, please refer to:**

- **Paper:** [KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding](https://arxiv.org/abs/2502.14949)
- **Code:** [https://github.com/mbzuai-oryx/KITAB-Bench](https://github.com/mbzuai-oryx/KITAB-Bench)
- **Project Page:** [https://mbzuai-oryx.github.io/KITAB-Bench/](https://mbzuai-oryx.github.io/KITAB-Bench/)