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AI identifies writers of historical Arabic manuscripts with high accuracy

Researchers have developed a Convolutional Neural Network (CNN) with attention mechanisms to identify writers of historical Arabic manuscripts. The study, using the Muharaf dataset, expanded writer labels and established new baselines for writer identification under both line-level and page-disjoint evaluation protocols. The CNN model achieved high accuracy on line-level identification, demonstrating its potential for historical analysis and manuscript provenance. AI

影响 Provides a new benchmark and practical resource for historical document analysis and writer identification.

排序理由 Academic paper detailing a new model and dataset evaluation for writer identification.

在 arXiv cs.CV 阅读 →

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AI identifies writers of historical Arabic manuscripts with high accuracy

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Hamza A. Abushahla, Ariel Justine N. Panopio, Layth Al-Khairulla, Mohamed I. AlHajri ·

    Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts

    arXiv:2604.22515v1 Announce Type: new Abstract: Handwritten Arabic manuscripts preserve the Arab world's intellectual and cultural heritage, and writer identification supports provenance, authenticity verification, and historical analysis. Using the Muharaf dataset of historical …

  2. arXiv cs.CV TIER_1 English(EN) · Mohamed I. AlHajri ·

    Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts

    Handwritten Arabic manuscripts preserve the Arab world's intellectual and cultural heritage, and writer identification supports provenance, authenticity verification, and historical analysis. Using the Muharaf dataset of historical Arabic manuscripts, we evaluate writer identific…