@inproceedings{patle-etal-2026-duwatbench,
    title = "{D}uwat{B}ench: Bridging Language and Visual Heritage through an {A}rabic Calligraphy Benchmark for Multimodal Understanding",
    author = "Patle, Shubham  and
      Ghaboura, Sara  and
      Tariq, Hania  and
      Khan, Mohammad Usman  and
      Thawakar, Omkar  and
      Anwer, Rao Muhammad  and
      Khan, Salman",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.243/",
    pages = "5256--5269",
    ISBN = "979-8-89176-380-7",
    abstract = "Arabic calligraphy represents one of the richest visual traditions of the Arabic language, blending linguistic meaning with artistic form. Although multimodal models have advanced across languages, their ability to process Arabic script, especially in artistic and stylized calligraphic forms, remains largely unexplored. To address this gap, we present DuwatBench, a benchmark of 1,272 curated samples containing about 1,475 unique words across 6 classical and modern calligraphic styles, each paired with sentence-level detection annotations. The dataset reflects real-world challenges in Arabic writing, such as complex stroke patterns, dense ligatures, and stylistic variations that often challenge standard text recognition systems.Using DuwatBench, we evaluated 13 leading Arabic and multilingual multimodal models and showed that while they perform well in clean text, they struggle with calligraphic variation, artistic distortions, and precise visual{--}text alignment. By publicly releasing DuwatBench and its annotations, we aim to advance culturally grounded multimodal research, foster fair inclusion of Arabic language and visual heritage in AI systems, and support continued progress in this area. Our dataset and code are publicly available."
}