Historic Arabic handwritten texts present significant challenges due to varied handwriting styles, cursive structure, diverse diacritics, and inconsistent character and word sizes. In this work, we introduce Historic-Arabic-OCR, a vision-language OCR system built upon Qari-OCR, which itself is based on Qwen2-VL-2B-Instruct, and further fine- tuned using Low-Rank Adaptation (LoRA) for Arabic manuscript transcription. The proposed approach incorporates contrast enhancement using CLAHE and deterministic decoding strategies to reduce character-level errors. Our model achieves competitive performance, with a Word Error Rate (WER) of 0.28 and a Character Error Rate (CER) of 0.10 on historical Arabic texts, including low-resolution images. The final submitted system uses CLAHE prepro- cessing with deterministic greedy decoding to minimize character-level errors. Keywords: Arabic OCR, Vision-Language Models, Qwen2-VL, LoRA, CER Optimization