@inproceedings{islam-etal-2026-bornodrishti,
    title = "{B}orno{D}rishti: Leveraging Vision Encoders and Domain-Adaptive Learning for {B}angla {OCR} on Diverse Documents",
    author = "Islam, S M Jishanul  and
      Hasan, Md Mehedi  and
      Ovi, Masbul Haider  and
      Rabby, Akm Shahariar Azad  and
      Rahman, Fuad",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.20/",
    pages = "278--286",
    ISBN = "979-8-89176-384-5",
    abstract = "OCR for Bangla scripts remains a challenging problem, with existing solutions limited to single-domain processing. Current approaches lack a unified vision encoder that can understand diverse Bangla script variations, hindering practical deployment. We present BornoDrishti, the first unified OCR system based on the vision transformer that accurately recognizes both printed and handwritten Bangla scripts within a single model. Our approach introduces a novel domain objective that enables the model to learn domain-invariant representations while preserving script-specific features, eliminating the need for separate domain experts. BornoDrishti achieves competitive accuracy across both domains, setting state-of-the-art performance for printed scripts and demonstrating that a single unified model can match or exceed specialized uni-domain systems. We evaluate our model against state-of-the-art domain-specific and cross-domain OCR systems. This work establishes a foundation for advancing practical applications by using a unified multi-domain OCR system for complex Bangla scripts."
}