The paper presents an automated pipeline that generates high‑quality Quranic datasets, including 848 hours of audio and 286,000 annotated utterances, by collecting recitations, segmenting at pause points with a fine‑tuned wav2vec2‑BERT model, transcribing segments, and verifying transcripts using a novel Tasmeea algorithm. It introduces qdat_bench, a benchmark covering phonemes, diacritization, and Tajweed rules, and a custom Quran Phonetic Script (QPS) for encoding Tajweed. A multi‑level CTC model trained on this data achieves a 0.21% phoneme error rate on the test set and 1.94% on qdat_bench, with a 75.8% Tajweed F1 score.
By Abdullah Abdelfattah, Mahmoud I. Khalil, Hazem Abbas
arXiv:2609.22038v1 Announce Type: new
Abstract: We introduce QuranicMMLU, a benchmark for evaluating generative AI on Quranic Arabic across multiple dimensions of linguistic complexity. Existing Qura...
By Rawan El Ghali, Umm Kulsoom, Anas Madkoor, Dima Faris Alsaudi, Roaa Abdelmagid, Roaa Ibrahim, Raghad Mousa, Hamza Aljaji, Abdullah Khanafer, Abdallah Alkanani, Salah Feras Alali, Rawan Khaled Mohamed, Ehsaneddin Asgari
The paper presents a human‑annotated dataset of 100 Quran recitation recordings, identifying 348 scored units and 162 localized events across ten combined labels. An evaluator that scores both labels and word positions achieves a label‑aware F1 of 0.525 and a localization F1 of 0.826, with adapted production cleaner/alignment components yielding similar scores. A pilot experiment with eight 20‑minute runs across three coders and eight models shows wide variance in F1 (0.143–0.892) and highlights that most gold events are detected, leaving only span extent and label conventions as the remaining challenges.
By Mohamad Al Mdfaa, Nursultan Askarbekuly, Ahmed Helaly, Ubai Sandouk, Manuel Mazzara
arXiv:2606. 19747v1 Announce Type: new Abstract: Quran Automatic Speech Recognition (ASR) aims to convert Quranic recitation into text, enabling applications such as aided memorisation tools and Quranic search engines.
By Nabil Mosharraf Hossain (Greentech Apps Foundation, United Kingdom), Riasat Islam (Greentech Apps Foundation, United Kingdom, Queen Mary University of London, United Kingdom), Unaizah Obaidellah (University of Malaya, Malaysia)
AraMS-28k is the largest publicly released line‑level dataset of genuine historical Arabic manuscripts, containing 14 books, 3,043 pages, and 28,600 annotated text lines (27,971 main‑text and 629 margin). The dataset spans three script traditions—Naskh, Ruq'ah, and Maghrebi—and includes a lithographed printed edition for format diversity. Each line is labeled as main‑text or margin, with margin lines that have a clear attachment point annotated with an insertion anchor to recover the manuscript’s true non‑linear reading order; both fully vocalized and diacritic‑normalized transcriptions are provided, and the data was produced via the RefLAM pipeline combining OCR, clean transcriptions, and human review.
"whyItMatters":"The dataset’s comprehensive line‑level annotations, including reading‑order anchors and dual transcription formats, enable reproducible research on Arabic manuscript recognition, layout analysis, and reading‑order recovery under a CC BY‑NC‑SA 4.0 license."
By Mohamed Guechaoui, Mohamed Diaa Zellagui, Souleyman Chaib, Sahraoui Dhelim
arXiv:2609.17539v1 Announce Type: new
Abstract: We present MudawanSn, a gold-standard resource of 1,271 sentence-aligned pairs manually translated from Wolof into Modern Standard Arabic (MSA). The so...
By Mouhamed Mbaye, Thierno Diop
arXiv:2608. 13580v1 Announce Type: cross Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report.
By Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim, Mostafa Awad, Abdelrahman Sadallah, Gurpreet Gosal, Gokulakrishnan Ramakrishnan, Sarath Chandran, Biswajit Mishra, Rituraj Joshi, Ahmed Frikha, Etienne Goffinet, Abhishek Maiti, Ali El Filali, Sarah AlBarri, Samujjwal Ghosh, Rahul Pal, Parvez Mullah, Awantika Shukla, Sajid siddiki, Samta Kamboj, Onkar Pandit, Sunil Kumar Sahu, AbdelRahman Elbadawy, Amr Mohamed, Ahmad Chamma, Evan Dufraisse, Abdelaziz Bounhar, Dani Bouch, Hadi Abdine, Guokan Shang, Fajri Koto, Yuxia Wang, Zhuohan Xie, Ali Mekky, Rania Elbadry, Sarfraz Ahmad, Momina Ahsan, Omar El Herraoui, Daniil Orel, Hasan Iqbal, Kareem Elzeky, Mervat Abassy, Kareem Elozeiri, Saadeldine Eletter, Farah Atif, Nurdaulet Mukhituly, Haonan Li, Xudong Han, Aaryamonvikram Singh, Zainul Abedien Ahmed Quraishi, Neha Sengupta, Larry Murray, Avraham Sheinin, Joel Hestness, Natalia Vassilieva, Hector Xuguang Ren, Zhengzhong Liu, Michalis Vazirgiannis, Preslav Nakov
RefLAM is a pipeline that converts manuscript page images and clean transcriptions into validated, line-level ground truth for Arabic handwritten text recognition. It combines a deep‑learning page‑segmentation model, a multimodal large language model for structured OCR, and a diacritic‑agnostic fuzzy alignment engine that assigns a confidence score to each line, with a provable correctness guarantee for perfect scores. Using RefLAM, the authors achieved a 75× speedup over manual annotation and released AraMS‑28k, a dataset of 14 historical Arabic manuscripts with detailed annotations.
By Mohamed Guechaoui, Mohamed Diaa Zellagui, Souleyman Chaib, Sahraoui Dhelim
The paper evaluates a multilingual ASR model (MMS‑1B‑all) on a Garrusi Kurdish dataset using a common‑reference staged normalization approach. By normalizing both reference and hypothesis, the authors show that raw Arabic‑script hypotheses yield a 111.70 % WER, which drops to 97.85 % after folding into a reduced orthography, highlighting the impact of orthographic differences on error measurement. A Southern Kurdish fine‑tuned system performs worse, and residual errors are partly due to scoring‑pipeline limitations rather than recognition failures.
By Hiwa Asadpour
arXiv:2606. 15883v1 Announce Type: cross Abstract: Kashmiri, an Indo-Aryan language written in a modified Perso-Arabic script, frequently omits diacritic marks in digital text, creating ambiguity and challenging downstream NLP applications.
By Haq Nawaz Malik, Nahfid Nissar, Faizan Iqbal
Ansari is a retrieval‑grounded Islamic AI assistant that has handled over 140,000 conversations in more than 25 languages since June 2023. It uses an agentic retrieval loop where a language model searches authenticated Islamic corpora—including the Qur’an, hadith collections, fiqh encyclopedias, and tafsir sources—and answers only based on retrieved content, providing citations for verification. The paper details Ansari’s architecture, multi‑platform deployment, evaluation results (including top performance on the IslamicMMLU leaderboard and strong resistance to false premises), and lessons for faith‑sensitive LLM deployments.
By M Waleed Kadous, Amr Elsayed, Abdullah Al Nahas, Ashraf Haress
The article reviews how transformer models, retrieval‑grounded pipelines, and large language models are reshaping hadith computational science. It critically evaluates existing literature, highlighting uneven progress: expanded data resources and mature segmentation tasks, yet persistent issues such as narrow corpora, weak benchmark comparability, and limited reproducibility. The authors argue that hadith computation should be viewed as an evidence‑infrastructure problem requiring knowledge integration, provenance, and expert supervision, and they propose a research agenda to strengthen the field’s methodological rigor.
By Md. Ashraful Haque (Greentech Apps Foundation, United Kingdom), Riasat Islam (Greentech Apps Foundation, United Kingdom, Queen Mary University of London, London, United Kingdom)