Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition
gagan3012
Abstract
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.
Community
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 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.
Model and demo will be released soon
Impressive work🔥 Can't wait to see the model and demo on the hub!!
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Would like to test it out and compare it to eScriptorium.
For Arabic users check out my idea on Telegram
مجموعة الرقمنة العربية
We will gather all of the details in here for Arabic OCR Handwritten
https://docs.google.com/document/d/16Sy9OfHle2B9QPjV-Admo2z1yJ9glv1CMyi2AvIbtKQ/edit?usp=drivesdk
nice impressive work, when will the model be published?
There is another model that we are working on it, it just be out soon
Does it understand arabic numbers
Congratulations on this impressive achievement ! I would love to explore the model further—would it be possible to share it with me?
tks
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