  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.
