Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition
<|reference_start|>Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition: 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.<|reference_end|>
@article{bhatia2024qalam,
    title={Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting
  Recognition},
    author={Gagan Bhatia, El Moatez Billah Nagoudi, Fakhraddin Alwajih, Muhammad
  Abdul-Mageed},
    journal={arXiv preprint arXiv:2407.13559},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13559},
    primaryClass={cs.CV cs.AI cs.CL}
}