@inproceedings{hamoud-etal-2026-nakba,
title = "{NAKBA} {NLP} 2026: Shared Task on {A}rabic Handwritten Manuscript Understanding ({P}alestine Memory{--}Omar Al-Saleh Memoir)",
author = "Hamoud, Hadi and
Chamseddine, Ahmad Ali and
Shalash, Bilal and
Ben Abid, Firas and
Jarrar, Mustafa and
Abou Chakra, Chadi and
Ghanem, Bernard and
Zaraket, Fadi A.",
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-1.7/",
doi = "10.63317/3iakmct86er7",
pages = "70--79",
abstract = "Transcribing historical Arabic manuscripts into machine-readable text is essential for preserving cultural heritage and enabling computational research in the humanities, yet it remains a challenging task due to handwriting variability, page degradation, and the complexity of Arabic script. To advance research in this area, we introduce the NAKBA NLP 2026 shared task on Arabic manuscript understanding, comprising two complementary tracks: a manual transcription track, in which participating teams annotate unlabelled handwritten line images, and an automatic system track for handwritten text recognition (HTR). Both tracks use the Omar Al-Saleh Memoir Collection, a corpus of 6,395 scanned pages and approximately 1.6 million words, written between 1951 and 1965 and provided by the Palestine Memory Project. The dataset, evaluation scripts, and system outputs are publicly available.[\url{https://acr.ps/1L9BaeY}] In Subtask{~}1 (Transcription Track), three teams contributed manual line-level transcriptions; evaluation on hidden ground-truth samples yielded Character Error Rates (CER) between 0.06 and 0.11. In Subtask{~}2 (Systems Track), seven teams submitted HTR systems. The top-performing system, by Misraj{~}AI, achieved a corpus-level CER of 0.079 and Word Error Rate (WER) of 0.244, outperforming the organiser baseline (CER 0.368, WER 0.691). Rankings shift between corpus-level and per-line evaluation: the 3reeq team achieved the lowest per-line CER (0.082). All contributed transcriptions and system outputs are released under CC-BY-4.0 to support continued research in Arabic manuscript recognition and digital humanities."
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<abstract>Transcribing historical Arabic manuscripts into machine-readable text is essential for preserving cultural heritage and enabling computational research in the humanities, yet it remains a challenging task due to handwriting variability, page degradation, and the complexity of Arabic script. To advance research in this area, we introduce the NAKBA NLP 2026 shared task on Arabic manuscript understanding, comprising two complementary tracks: a manual transcription track, in which participating teams annotate unlabelled handwritten line images, and an automatic system track for handwritten text recognition (HTR). Both tracks use the Omar Al-Saleh Memoir Collection, a corpus of 6,395 scanned pages and approximately 1.6 million words, written between 1951 and 1965 and provided by the Palestine Memory Project. The dataset, evaluation scripts, and system outputs are publicly available.[https://acr.ps/1L9BaeY] In Subtask 1 (Transcription Track), three teams contributed manual line-level transcriptions; evaluation on hidden ground-truth samples yielded Character Error Rates (CER) between 0.06 and 0.11. In Subtask 2 (Systems Track), seven teams submitted HTR systems. The top-performing system, by Misraj AI, achieved a corpus-level CER of 0.079 and Word Error Rate (WER) of 0.244, outperforming the organiser baseline (CER 0.368, WER 0.691). Rankings shift between corpus-level and per-line evaluation: the 3reeq team achieved the lowest per-line CER (0.082). All contributed transcriptions and system outputs are released under CC-BY-4.0 to support continued research in Arabic manuscript recognition and digital humanities.</abstract>
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%0 Conference Proceedings
%T NAKBA NLP 2026: Shared Task on Arabic Handwritten Manuscript Understanding (Palestine Memory–Omar Al-Saleh Memoir)
%A Hamoud, Hadi
%A Chamseddine, Ahmad Ali
%A Shalash, Bilal
%A Ben Abid, Firas
%A Jarrar, Mustafa
%A Abou Chakra, Chadi
%A Ghanem, Bernard
%A Zaraket, Fadi A.
%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 hamoud-etal-2026-nakba
%X Transcribing historical Arabic manuscripts into machine-readable text is essential for preserving cultural heritage and enabling computational research in the humanities, yet it remains a challenging task due to handwriting variability, page degradation, and the complexity of Arabic script. To advance research in this area, we introduce the NAKBA NLP 2026 shared task on Arabic manuscript understanding, comprising two complementary tracks: a manual transcription track, in which participating teams annotate unlabelled handwritten line images, and an automatic system track for handwritten text recognition (HTR). Both tracks use the Omar Al-Saleh Memoir Collection, a corpus of 6,395 scanned pages and approximately 1.6 million words, written between 1951 and 1965 and provided by the Palestine Memory Project. The dataset, evaluation scripts, and system outputs are publicly available.[https://acr.ps/1L9BaeY] In Subtask 1 (Transcription Track), three teams contributed manual line-level transcriptions; evaluation on hidden ground-truth samples yielded Character Error Rates (CER) between 0.06 and 0.11. In Subtask 2 (Systems Track), seven teams submitted HTR systems. The top-performing system, by Misraj AI, achieved a corpus-level CER of 0.079 and Word Error Rate (WER) of 0.244, outperforming the organiser baseline (CER 0.368, WER 0.691). Rankings shift between corpus-level and per-line evaluation: the 3reeq team achieved the lowest per-line CER (0.082). All contributed transcriptions and system outputs are released under CC-BY-4.0 to support continued research in Arabic manuscript recognition and digital humanities.
%R 10.63317/3iakmct86er7
%U https://aclanthology.org/2026.nakbanlp-1.7/
%U https://doi.org/10.63317/3iakmct86er7
%P 70-79
Markdown (Informal)
[NAKBA NLP 2026: Shared Task on Arabic Handwritten Manuscript Understanding (Palestine Memory–Omar Al-Saleh Memoir)](https://aclanthology.org/2026.nakbanlp-1.7/) (Hamoud et al., NakbaNLP 2026)
ACL
- Hadi Hamoud, Ahmad Ali Chamseddine, Bilal Shalash, Firas Ben Abid, Mustafa Jarrar, Chadi Abou Chakra, Bernard Ghanem, and Fadi A. Zaraket. 2026. NAKBA NLP 2026: Shared Task on Arabic Handwritten Manuscript Understanding (Palestine Memory–Omar Al-Saleh Memoir). In Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026, pages 70–79, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).