@inproceedings{alsaif-etal-2026-alsaifteam,
title = "{A}l{S}aif{T}eam at {AR}-{MS} {NAKBA}-{NLP} 2026: Building Expert-Quality Ground Truth for {A}rabic Handwritten Manuscripts",
author = "AlSaif, Joud Fahad and
Mohammed, Alhasan Hamood and
Alseed, Jana Mohammad",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.39/",
doi = "10.63317/5pcgw3fzbc6i",
pages = "262--264",
abstract = "This paper describes our participation in Subtask 1 of the NAKBA NLP 2026 Arabic Manuscript Understanding Shared Task, which focuses on the manual creation of expert-quality, line-level transcriptions for Arabic handwritten manuscripts. To ensure reliable ground truth, we adopt a protocol-driven methodology based on fixed transcription rules, collaborative verification, and confidence-based quality control. The proposed approach aims to improve consistency, reduce annotation bias, and support the creation of trustworthy benchmark resources for future Arabic OCR and HTR research. Keywords:Arabic handwritten manuscripts, ground truth construction, manual transcription, handwritten text recognition, optical character recognition, benchmark enrichment"
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<abstract>This paper describes our participation in Subtask 1 of the NAKBA NLP 2026 Arabic Manuscript Understanding Shared Task, which focuses on the manual creation of expert-quality, line-level transcriptions for Arabic handwritten manuscripts. To ensure reliable ground truth, we adopt a protocol-driven methodology based on fixed transcription rules, collaborative verification, and confidence-based quality control. The proposed approach aims to improve consistency, reduce annotation bias, and support the creation of trustworthy benchmark resources for future Arabic OCR and HTR research. Keywords:Arabic handwritten manuscripts, ground truth construction, manual transcription, handwritten text recognition, optical character recognition, benchmark enrichment</abstract>
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%0 Conference Proceedings
%T AlSaifTeam at AR-MS NAKBA-NLP 2026: Building Expert-Quality Ground Truth for Arabic Handwritten Manuscripts
%A AlSaif, Joud Fahad
%A Mohammed, Alhasan Hamood
%A Alseed, Jana Mohammad
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F alsaif-etal-2026-alsaifteam
%X This paper describes our participation in Subtask 1 of the NAKBA NLP 2026 Arabic Manuscript Understanding Shared Task, which focuses on the manual creation of expert-quality, line-level transcriptions for Arabic handwritten manuscripts. To ensure reliable ground truth, we adopt a protocol-driven methodology based on fixed transcription rules, collaborative verification, and confidence-based quality control. The proposed approach aims to improve consistency, reduce annotation bias, and support the creation of trustworthy benchmark resources for future Arabic OCR and HTR research. Keywords:Arabic handwritten manuscripts, ground truth construction, manual transcription, handwritten text recognition, optical character recognition, benchmark enrichment
%R 10.63317/5pcgw3fzbc6i
%U https://aclanthology.org/2026.nakbanlp-1.39/
%U https://doi.org/10.63317/5pcgw3fzbc6i
%P 262-264
Markdown (Informal)
[AlSaifTeam at AR-MS NAKBA-NLP 2026: Building Expert-Quality Ground Truth for Arabic Handwritten Manuscripts](https://aclanthology.org/2026.nakbanlp-1.39/) (AlSaif et al., NakbaNLP 2026)
ACL