@inproceedings{ismail-etal-2023-hicma,
              title = "{HICMA}: The Handwriting Identification for Calligraphy and Manuscripts in {A}rabic Dataset",
              author = "Ismail, Anis  and
                Kamel, Zena  and
                Mahmoud, Reem",
              editor = "Sawaf, Hassan  and
                El-Beltagy, Samhaa  and
                Zaghouani, Wajdi  and
                Magdy, Walid  and
                Abdelali, Ahmed  and
                Tomeh, Nadi  and
                Abu Farha, Ibrahim  and
                Habash, Nizar  and
                Khalifa, Salam  and
                Keleg, Amr  and
                Haddad, Hatem  and
                Zitouni, Imed  and
                Mrini, Khalil  and
                Almatham, Rawan",
              booktitle = "Proceedings of ArabicNLP 2023",
              month = dec,
              year = "2023",
              address = "Singapore (Hybrid)",
              publisher = "Association for Computational Linguistics",
              url = "https://aclanthology.org/2023.arabicnlp-1.3",
              pages = "24--32",
              abstract = "Arabic is one of the most globally spoken languages with more than 313 million speakers worldwide. Arabic handwriting is known for its cursive nature and the variety of writing styles used. Despite the increase in effort to digitize artistic and historical elements, no public dataset was released to deal with Arabic text recognition for realistic manuscripts and calligraphic text. We present the Handwriting Identification of Manuscripts and Calligraphy in Arabic (HICMA) dataset as the first publicly available dataset with real-world and diverse samples of Arabic handwritten text in manuscripts and calligraphy. With more than 5,000 images across five different styles, the HICMA dataset includes image-text pairs and style labels for all images. We further present a comparison of the current state-of-the-art optical character recognition models in Arabic and benchmark their performance on the HICMA dataset, which serves as a baseline for future works. Both the HICMA dataset and its benchmarking tool are made available to the public under the CC BY-NC 4.0 license in the hope that the presented work opens the door to further enhancements of complex Arabic text recognition.",
          }
}