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.
669e3674dae53e4e045f4f8f
Gagan Bhatia
claimed_verified
2024-07-22T11:36:30.316Z
60394599033b61166496163b
https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
Gagan Bhatia
user
gagan3012
669e3674dae53e4e045f4f90
El Moatez Billah Nagoudi
669e3674dae53e4e045f4f91
Fakhraddin Alwajih
admin_assigned
2024-07-22T12:30:42.970Z
6265c5fe5909b93b24d797f1
/avatars/94f8e5f925c740d498c2709fe784a40b.svg
Fakhraddin Alwajih
user
Fakhraddin
669e3674dae53e4e045f4f92
Muhammad Abdul-Mageed
admin_assigned
2024-07-22T12:30:48.884Z
5feeae8dd8e0b2d24ed258cc
/avatars/1d66840db4ed6dc12bdee7ba8168ea9f.svg
Muhammad Abdul-Mageed
user
mageed
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.
Qalam, a foundation model using SwinV2 and RoBERTa architectures, achieves exceptional accuracy in Arabic OCR and HWR with low Word Error Rates and robust handling of diacritics and high-resolution inputs.
SwinV2 RoBERTa Arabic OCR Arabic HWR Arabic diacritics high-resolution inputs