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

Anjali Sarawgi, Esteban Garces Arias, Christof Zotter
12/18/2025
cs.LG

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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 explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy. Our best model achieves a Character Error Rate (CER) of 4.9\%. In addition, we implement and evaluate decoding strategies and analyze token-level confusions to better understand model behaviour and error patterns. While the dataset we used for evaluation is confidential, we release our training code, model configurations, and evaluation scripts to support further research in HTR for low-resource historical scripts.

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Cite this paper

@article{sarawgi2025digitizing,
  title  = {Digitizing Nepal's Written Heritage: A Comprehensive HTR Pipeline for Old Nepali Manuscripts},
  author = {Anjali Sarawgi and Esteban Garces Arias and Christof Zotter},
  year   = {2025},
  eprint = {2512.17111},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.17111v1}
}

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