Al-Warraq at AR-MS NAKBA-NLP 2026: Adapting Vision-Language and Transformer Models for Automatic Manuscript OCR/HTR
Year: 2026
Type: conference-paper
Abstract: We present our submission to the NAKBA NLP 2026 Automatic Manuscript OCR/HTR shared task on Arabic manuscripts.The task aims to transcribe manuscript line images into machine-readable Arabic text.Our approach followed an iterative pipeline including model selection, training, error analysis, test-time augmentation, and postprocessing.After evaluating several OCR/HTR models, we selected and trained the most suitable model on the provided manuscript line images and transcriptions.Error analysis showed better character-level performance than word-level performance, which motivated the use of test-time augmentation and text cleaning to improve robustness.The final system achieved a CER of 0.1142 and a WER of 0.378, placing fifth in the shared task.These results show that simple but targeted improvements can support effective Arabic manuscript transcription.
Institution: Arab Open University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166575220