This paper presents the Fine-Tashkeel system for Task 2 of the KSAA-2026 Shared Task on Automatic Diacritization of Speech Dictation. Diacritization of speech-derived Arabic text poses challenges due to dialectal variation, morphological ambiguity, and the absence of acoustic cues in text-only pipelines. Our approach treats diacritization as a character-level sequence-to-sequence task, mapping undiacritized text directly to its fully diacritized form. We evaluate 18 models spanning text-only, ASR-augmented, and fine-tuned configurations, finding that text-only Seq2Seq approaches outperform off-the-shelf multimodal models—a gap we attribute to task mismatch in generic ASR systems rather than an inherent audio limitation. Our best submission, using zero-shot inference without task-specific training, achieved a Diacritic Error Rate (DER) of 10.56%, Word Error Rate (WER) of 34.47%, and Sentence Error Rate (SER) of 79.88%, ranking 5th out of 7 teams. Per-nationality error analysis reveals significant dialectal variation (Egyptian 3.70% vs. Algerian 13.73% DER), and diagnostic analysis confirms that case endings and vowel ambiguity are the primary bottlenecks. Code and evaluation scripts are publicly available.

This paper presents Ketaba-OCR-LoRA, a system developed for the NakbaNLP 2026 Shared Task on Arabic Manuscript Understanding (Subtask 2), which targets the transcription of the historically significant Omar Al-Saleh Memoir Collection written in Ruq’ah and Naskh scripts. We propose a parameter-efficient adaptation of a publicly available pretrained Arabic-English Handwritten Text Recognition (HRT) model, originally trained on handwritten corpora including the Muharaf dataset. Instead of adapting general Vision-Language Models from scratch, we fine-tune the HRT backbone using Low-Rank Adaptation (LoRA) and 4-bit quantization (QLoRA), reducing memory requirements from 40GB to approximately 8GB. Our final submission combines multiple model variants through a novel Linear+Boost weighted ensemble strategy. Our approach achieves a CER of 0.0819 and WER of 0.2588 on the blind test set (per-line evaluation), ranking 1st on per-line evaluation; on the official corpus-wide leaderboard, we rank 3rd (CER 0.0938, WER 0.2996). This work demonstrates that specialized pretrained HRT models substantially outperform general-purpose Vision-Language Models for Arabic manuscript transcription, and that parameter-efficient fine-tuning provides a practical and reproducible approach for low-resource cultural heritage digitization.