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HTR pipeline digitizes historical Nepali manuscripts with 4.9% error rate

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new end-to-end pipeline for Handwritten Text Recognition (HTR) specifically designed for Old Nepali manuscripts. This system aims to digitize a historically significant but low-resource language. The best performing model achieved a Character Error Rate (CER) of 4.9%, and the team has released their training code and evaluation scripts to encourage further research in this area. AI

IMPACT Enables digitization of low-resource historical scripts, potentially unlocking new avenues for linguistic and historical research.

RANK_REASON This is a research paper presenting a new pipeline for Handwritten Text Recognition on historical manuscripts.

Read on arXiv cs.LG →

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

HTR pipeline digitizes historical Nepali manuscripts with 4.9% error rate

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This is a research paper presenting a new pipeline for Handwritten Text Recognition on historical manuscripts.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anjali Sarawgi, Esteban Garces Arias, Christof Zotter ·

    Digitizing Nepal's Written Heritage: A Comprehensive HTR Pipeline for Old Nepali Manuscripts

    arXiv:2512.17111v2 Announce Type: replace Abstract: This paper presents the first end-to-end pipeline for Handwritten Text Recognition (HTR) for Old Nepali, a historically significant but low-resource language. We adopt a line-level transcription approach and systematically explo…

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