Work

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

Year: 2026

Type: conference-paper

Abstract: This paper presents the first end-to-end pipeline for Handwritten Text Recognition (HTR) for Old Nepali, a historically significant but lowresource 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 tokenlevel confusions to better understand model behavior and error patterns.Although the evaluation dataset is confidential, we release our training code, model configurations, and evaluation scripts to support further research on HTR for low-resource historical scripts.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.671

Authors: Anjali Sarawgi, Esteban Garces Arias, Christof Zotter

Institutions: Ludwig-Maximilians-Universität München, Munich Center for Machine Learning, Heidelberg Academy of Sciences and Humanities

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Heidelberger Akademie der Wissenschaften

OpenAlex ID: W7166825189