arXiv:2607.00250v1 Announce Type: new Abstract: Maltese has decent text corpora and pretrained language models, but, like many languages outside the handful with large OCR benchmarks, only a single known real labelled PDF corpus for OCR training, 57 page, far below what paragraph-level training needs: low-resource for OCR specifically. With no real corpus to train on at scale, we built a synthetic training pipeline and a 5-stream Tesseract LV-ROVER ensemble, and report results on a 422-paragraph benchmark against a fine-tuned-Tesseract baseline of character error rate (CER) 0.0234. Ensemble re
The proliferation of AI and the need for greater linguistic inclusivity drives innovation in low-resource language processing.
Improving OCR for low-resource languages can unlock vast amounts of previously inaccessible data, fostering digital inclusion and enabling new AI applications.
The ability to accurately convert Maltese historical documents and texts into digital formats is significantly enhanced, creating a precedent for other low-resource languages.
- · Maltese language users
- · Linguists and historians
- · AI developers in low-resource contexts
- · Digital archives
- · Monolingual data processing systems
- · Traditional manual data entry services
Digitization of Maltese cultural heritage and administrative records accelerates.
Similar OCR advancements are prioritized and developed for other European and global low-resource languages, reducing linguistic digital divides.
Enhanced OCR capabilities contribute to the development of more diverse and robust sovereign AI initiatives, utilizing a broader range of language data.
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