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New dataset and pipeline advance Arabic manuscript OCR research

By PulseAugur Editorial · [4 sources] ·

Researchers have introduced AraMS-28k, a large-scale dataset for historical Arabic manuscript recognition, featuring 28,600 annotated text lines across 14 books. This dataset includes detailed annotations for main text and margin lines, with insertion anchors to reconstruct non-linear reading order. The dataset was created using RefLAM, a novel pipeline that leverages multimodal large language models and human review to achieve a 75x throughput gain over manual annotation. Additionally, a study explored the effectiveness of Vision-Language Models (VLMs) for Arabic manuscript OCR, finding that their utility varies depending on the dataset and the specific task, with OCR-conditioned VLM correction showing promise in certain scenarios. AI

IMPACT Advances in Arabic manuscript OCR and annotation pipelines could improve access to historical texts and support digital humanities research.

RANK_REASON Multiple research papers introducing new datasets, annotation pipelines, and evaluation methodologies for Arabic manuscript OCR.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New dataset and pipeline advance Arabic manuscript OCR research

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COVERAGE [4]

  1. arXiv cs.CL TIER_1 English(EN) · Mohamed Guechaoui, Mohamed Diaa Zellagui, Souleyman Chaib, Sahraoui Dhelim ·

    AraMS-28k: The Largest Publicly Released Line-Level Dataset of Historical Arabic Manuscripts with Margin and Insertion-Anchor Annotations

    arXiv:2608.26921v1 Announce Type: cross Abstract: We introduce AraMS-28k, the largest publicly released line-level dataset of genuine historical Arabic manuscripts, comprising 14 books, 3,043 pages, and 28,600 annotated text lines (27,971 main-text, 629 margin). Thirteen books ar…

  2. arXiv cs.CL TIER_1 English(EN) · Mohamed Guechaoui, Mohamed Diaa Zellagui, Souleyman Chaib, Sahraoui Dhelim ·

    RefLAM: A Reference-Grounded Line Annotation Pipeline for Historical Arabic Manuscripts

    arXiv:2608.25140v1 Announce Type: cross Abstract: Existing approaches to building line-level Arabic handwritten-text-recognition (HTR) training data either rely on fully manual annotation, which does not scale, or on automatic OCR-to-reference alignment methods not yet extended t…

  3. arXiv cs.CV TIER_1 English(EN) · Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji, Zakaria Maamar, Hamdy Mubarak, Wajdi Zaghouani ·

    When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study

    arXiv:2608.22366v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remains underexplored. To address such a gap in this paper, we evaluate traditional OC…

  4. arXiv cs.CV TIER_1 English(EN) · Sumaih Almarshad, Maram Alamri, Dona Aloraini, Fares Altuwaim, AlJawharh AlOtaibi, Reem Alyabis, Rayah Aldawsari ·

    Does a Modern-Handwriting Warm-Up Help Historical Arabic OCR? A Reproducible, Compute-Matched Evaluation on Muharaf and KHATT

    arXiv:2608.22316v1 Announce Type: cross Abstract: Whether an intermediate stage of modern Arabic handwriting helps or hurts historical Arabic HTR is usually decided from one implementation and one comparison, too thin a basis for a claim either way. We test stability by running t…

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