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.

We present ImageEval 2025, the first shared task dedicated to Arabic image captioning. The task addresses the critical gap in multimodal Arabic NLP by focusing on two complementary subtasks: (1) creating the first open-source, manually-captioned Arabic image dataset through a collaborative datathon, and (2) developing and evaluating Arabic image captioning models. A total of 44 teams registered, of which eight submitted during the test phase, producing 111 valid submissions. Evaluation was conducted using automatic metrics, LLM-based judgment, and human assessment. In Subtask 1, the best-performing system achieved a cosine similarity of 65.5, while in Subtask 2, the top score was 60.0. Although these results show encouraging progress, they also confirm that Arabic image captioning remains a challenging task, particularly due to cultural grounding requirements, morphological richness, and dialectal variation. All datasets, baseline models, and evaluation tools are released publicly to support future research in Arabic multimodal NLP.