@inproceedings{heakl-etal-2025-kitab,
    title = "{KITAB}-Bench: A Comprehensive Multi-Domain Benchmark for {A}rabic {OCR} and Document Understanding",
    author = "Heakl, Ahmed  and
      Sohail, Muhammad Abdullah  and
      Ranjan, Mukul  and
      Elbadry, Rania  and
      Ahmad, Ghazi Shazan  and
      El-Geish, Mohamed  and
      Maher, Omar  and
      Shen, Zhiqiang  and
      Khan, Fahad Shahbaz  and
      Khan, Salman",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1135/",
    pages = "22006--22024",
    ISBN = "979-8-89176-256-5",
    abstract = "With the growing adoption of Retrieval-Augmented Generation (RAG) in document processing, robust text recognition has become increasingly critical for knowledge extraction. While OCR (Optical Character Recognition) for English and other languages benefits from large datasets and well-established benchmarks, Arabic OCR faces unique challenges due to its cursive script, right-to-left text flow, and complex typographic and calligraphic features. We present KITAB-Bench, a comprehensive Arabic OCR benchmark that fills the gaps in current evaluation systems. Our benchmark comprises 8,809 samples across 9 major domains and 36 subdomains, encompassing diverse document types including handwritten text, structured tables, and specialized coverage of 21 chart types for business intelligence. Our findings show that modern vision language models (such as GPT-4o, Gemini, and Qwen) outperform traditional OCR approaches (such as EasyOCR, PaddleOCR, and Surya) by an average of 60{\%} in the character error rate (CER). Furthermore, we highlight significant limitations of current Arabic OCR models, particularly in PDF-to-Markdown conversion, where the best model Gemini-2.0-Flash achieves only 65{\%} accuracy. This underscores the challenges of accurately recognizing Arabic text, including issues with complex fonts, numeral recognition errors, word elongation, and table structure detection. This work establishes a rigorous evaluation framework that can drive improvements in Arabic document analysis methods and bridge the performance gap with English OCR technologies."
}