Misraj AI at AR-MS NAKBA-NLP 2026: A State-of-the-Art VLM in Arabic Handwritten Text Recognition
Year: 2026
Type: conference-paper
Abstract: Handwritten Text Recognition (HTR) for Arabic presents unique challenges due to the script's cursive nature, varying writer styles, and morphological complexity.While modern Vision-Language Models (VLMs) have significantly advanced document parsing, their direct application to highly specific cursive domains requires strategic adaptation.This paper details our submission to the Nakba OCR competition, which adapts a 3B-parameter VLM to recognize historical Arabic manuscripts.We employ a progressive training pipeline that utilizes domain-matched data augmentation to bridge the gap between standard printed Arabic OCR and historical handwritten manuscripts.Moving beyond standard decoder-only Supervised Fine-Tuning (SFT), we fine-tune the entire encoder-decoder architecture using differential learning rates.This approach, followed by a final checkpoint merge, allows the model to better resolve the fine visual details of cursive Arabic script.Our final unified model (submitted under the team name Misraj AI) establishes a new state-of-the-art (SOTA) on the Nakba dataset, achieving a Word Error Rate (WER) of 0.24 and a Character Error Rate (CER) of 0.08, and officially securing first place on the leaderboard.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166519090