The Arabic-English-handwritten-OCR-v3 is an advanced OCR model designed to extract handwriting from images in Arabic, English, and multiple other languages. Built on Qwen/Qwen2.5-VL-3B-Instruct and fine-tuned with 47,842 specialized samples, it achieves a remarkable Character Error Rate (CER) of 1.78%, significantly outperforming commercial solutions like Google Vision API by 57%. The model's training is currently focused on Naskh, Ruq'ah, and Maghrebi scripts, with potential expansion to other scripts and over 30 languages. A key scientific discovery during its development is the "Dynamic Equilibrium Theorem," which enhances model training efficiency and accuracy by stabilizing evaluation loss and adapting train loss dynamically, setting a new theoretical benchmark for model training. This matters because it represents a significant advancement in OCR technology, offering more accurate and efficient solutions for multilingual handwritten text recognition.
