@inproceedings{yousuf-etal-2026-dataset,
    title = "A Dataset of {W}olof Ajami Manuscripts for {HTR} and {OCR}",
    author = "Yousuf, Oreen  and
      Diagne, Elhadji Djibril  and
      H{\o}gel, Christian  and
      Megyesi, Beata  and
      Nivre, Joakim",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/cawl-year/2026.lrec-1.253/",
    doi = "10.63317/4pz98ojeeqpw",
    pages = "3234--3239",
    abstract = "We present the first ever dataset of manually segmented and transcribed Ajami manuscripts written in Wolof. The term Ajami refers to modified Arabic-script orthographies used to transcribe African languages. Handwritten text recognition (HTR) and optical character recognition (OCR) models for Arabic-script languages perform poorly on African languages written in Ajami orthographies because these languages are not represented in the pre-training data of the models. This leads to recognition models being unable to extract unique Arabic-script letters and ubiquitous diacritics used in African languages, and struggling to adapt to various calligraphy styles used across Africa. We release the following as an open-source dataset: an ALTO formatting of high-quality images of handwritten and printed, 20th{--}century Wolof manuscripts; manual segmentation (region and line); and manual transcriptions. We extend our contribution by evaluating several Arabic-script recognition models intended for historical manuscripts and find they produce character error rates (CER) of 61{--}81{\%}. Transcriptions produced by the evaluated recognition models, as well as a keyboard to transcribe Wolof Ajami manuscripts, are released as well. The digitally transcribed text in the dataset can also be utilized for various natural language processing (NLP) and historical linguistic tasks."
}