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New framework builds OCR for low-resource scripts using synthetic data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel data-centric framework capable of building Optical Character Recognition (OCR) systems for low-resource scripts without requiring any manually annotated document images. The system utilizes an unannotated text corpus and digital fonts to generate synthetic training data, preserving script-specific visual structures. This approach was successfully applied to Kashmiri, a Perso-Arabic Nastaliq script, achieving a character error rate of 0.39% on held-out words and establishing new reference results on various document types, including historical documents. AI

IMPACT Enables OCR for previously unsupported languages, potentially unlocking vast amounts of text data for analysis and digitization.

RANK_REASON The item is an academic paper detailing a new framework and methodology for OCR development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework builds OCR for low-resource scripts using synthetic data

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The item is an academic paper detailing a new framework and methodology for OCR development. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Faizan Iqbal, Haq Nawaz Malik, Hafsah Pervaiz ·

    From Zero to OCR: A Data-Centric Framework for Unsupported Low-Resource Scripts

    arXiv:2611.00078v1 Announce Type: cross Abstract: Optical character recognition (OCR) remains inaccessible to many low-resource scripts because annotated document images are unavailable and synthetic training data can fail to preserve script-specific visual structure. We present …

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