Qalam: A Multimodal LLM for Arabic Optical Character and Handwriting Recognition
Gagan Bhatia, El Moatez Billah Nagoudi, Fakhraddin Alwajih, Muhammad Abdul-Mageed
Abstract
Arabic Optical Character Recognition (OCR) and Handwriting Recognition (HWR) pose unique challenges due to the cursive and context-sensitive nature of the Arabic script.This study introduces Qalam, a novel foundation model designed for Arabic OCR and HWR, built on a SwinV2 encoder and RoBERTa decoder architecture.Our model significantly outperforms existing methods, achieving a Word Error Rate (WER) of just 0.80% in HWR tasks and 1.18% in OCR tasks.We train Qalam on a diverse dataset, including over 4.5 million images from Arabic manuscripts and a synthetic dataset comprising 60k image-text pairs.Notably, Qalam demonstrates exceptional handling of Arabic diacritics, a critical feature in Arabic scripts.Furthermore, it shows a remarkable ability to process high-resolution inputs, addressing a common limitation in current OCR systems.These advancements underscore Qalam's potential as a leading solution for Arabic script recognition, offering a significant leap in accuracy and efficiency.