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---
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1473584741.0
num_examples: 200
download_size: 1276133511
dataset_size: 1473584741.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- table-question-answering
license: apache-2.0
---
# 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 capabilities. It covers nine major domains and 36 sub-domains across 8,809 samples, including diverse document types like PDFs, handwritten text, structured tables, and financial & legal reports. The benchmark evaluates performance across various tasks, including OCR, layout detection, table recognition, chart extraction, and PDF conversion. Novel evaluation metrics are introduced to ensure rigorous assessment across multiple dimensions.
**Key Highlights:**
* 9 major domains & 36 sub-domains across 8,809 samples.
* 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: Markdown Recognition (MARS), Table Edit Distance (TEDS), Chart Data Extraction (SCRM).
**Please see paper & code for more information:**
- [Paper](https://arxiv.org/abs/2502.14949)
- [Code](https://github.com/mbzuai-oryx/KITAB-Bench)
- [Project Page](https://mbzuai-oryx.github.io/KITAB-Bench/) |