Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition

Gagan Bhatia, El Moatez Billah Nagoudi, Fakhraddin Alwajih, Muhammad Abdul-Mageed

Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition: 23 upvotes on Hugging Face Daily Papers, #11 of 16 papers on 2024-07-22. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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