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Adapting multilingual vision language transformers for low-resource Urdu optical character recognition (OCR).
Cheema, Musa Dildar Ahmed; Shaiq, Mohammad Daniyal; Mirza, Farhaan; Kamal, Ali; Naeem, M Asif.
Afiliación
  • Cheema MDA; Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
  • Shaiq MD; Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
  • Mirza F; School of Computer, Engineering and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
  • Kamal A; Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
  • Naeem MA; Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
PeerJ Comput Sci ; 10: e1964, 2024.
Article en En | MEDLINE | ID: mdl-38699211
ABSTRACT
In the realm of digitizing written content, the challenges posed by low-resource languages are noteworthy. These languages, often lacking in comprehensive linguistic resources, require specialized attention to develop robust systems for accurate optical character recognition (OCR). This article addresses the significance of focusing on such languages and introduces ViLanOCR, an innovative bilingual OCR system tailored for Urdu and English. Unlike existing systems, which struggle with the intricacies of low-resource languages, ViLanOCR leverages advanced multilingual transformer-based language models to achieve superior performances. The proposed approach is evaluated using the character error rate (CER) metric and achieves state-of-the-art results on the Urdu UHWR dataset, with a CER of 1.1%. The experimental results demonstrate the effectiveness of the proposed approach, surpassing state of the-art baselines in Urdu handwriting digitization.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PeerJ Comput Sci Año: 2024 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PeerJ Comput Sci Año: 2024 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos