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---
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/) |