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

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arxiv 2407.13559 v1 pith:CBRGVPPB submitted 2024-07-18 cs.CV cs.AIcs.CL

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

Gagan Bhatia , El Moatez Billah Nagoudi , Fakhraddin Alwajih , Muhammad Abdul-Mageed This is my paper
classification cs.CV cs.AIcs.CL
keywords arabicqalamrecognitioncharacterdatasethandwritingmodeloptical
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation

    cs.CL 2025-09 reject novelty 5.0

    An AI-driven, self-refining loop generates multi-page Arabic QA pairs and a new benchmark, but the claimed gains over static pipelines are not demonstrated.

  2. LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4

    cs.CV 2026-07 reject novelty 4.0

    An LLM-driven closed-loop neural architecture search is applied to Arabic, Persian, and English handwriting, claiming mean test accuracies above 93% and 41–44 ms inference.