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LV-ROVER ensemble boosts Maltese OCR accuracy by 70%

By PulseAugur Editorial · [2 sources] ·

Researchers have developed LV-ROVER, a novel multi-stream Tesseract voting ensemble designed to improve optical character recognition (OCR) for Maltese, a low-resource language. By building a synthetic training pipeline and a five-stream ensemble, they achieved a 44% improvement in character error rate (CER) compared to a fine-tuned Tesseract baseline. Further post-processing reduced the CER by 70%, demonstrating significant gains in OCR accuracy for Maltese text. AI

IMPACT Improves OCR capabilities for low-resource languages, potentially enabling broader access to digitized text.

RANK_REASON Academic paper detailing a new method for OCR on a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LV-ROVER ensemble boosts Maltese OCR accuracy by 70%

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Academic paper detailing a new method for OCR on a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Adam Darmanin ·

    LV-ROVER: Multi-Stream Tesseract Voting for Maltese Paragraph OCR

    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…

  2. arXiv cs.CL TIER_1 English(EN) · Adam Darmanin ·

    LV-ROVER: Multi-Stream Tesseract Voting for Maltese Paragraph OCR

    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 spec…

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