title: Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting
    Recognition
summary: 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_authors._id: 669e3674dae53e4e045f4f8f
paper_authors.name: Gagan Bhatia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-22T11:36:30.316Z
paper_authors._id: 60394599033b61166496163b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
paper_authors.fullname: Gagan Bhatia
paper_authors.type: user
paper_authors.user: gagan3012
paper_authors._id: 669e3674dae53e4e045f4f90
paper_authors.name: El Moatez Billah Nagoudi
paper_authors._id: 669e3674dae53e4e045f4f91
paper_authors.name: Fakhraddin Alwajih
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-22T12:30:42.970Z
paper_authors._id: 6265c5fe5909b93b24d797f1
paper_authors.avatarUrl: /avatars/94f8e5f925c740d498c2709fe784a40b.svg
paper_authors.fullname: Fakhraddin Alwajih
paper_authors.type: user
paper_authors.user: Fakhraddin
paper_authors._id: 669e3674dae53e4e045f4f92
paper_authors.name: Muhammad Abdul-Mageed
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-22T12:30:48.884Z
paper_authors._id: 5feeae8dd8e0b2d24ed258cc
paper_authors.avatarUrl: /avatars/1d66840db4ed6dc12bdee7ba8168ea9f.svg
paper_authors.fullname: Muhammad Abdul-Mageed
paper_authors.type: user
paper_authors.user: mageed
paper_title: Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting
    Recognition
paper_summary: 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_ai_keywords: SwinV2 encoder RoBERTa decoder Word Error Rate (WER) Arabic OCR Arabic HWR Arabic manuscripts Arabic diacritics high-resolution inputs