# Quran Recitation Recognition using End-to-End Deep Learning

arXiv: https://arxiv.org/abs/2305.07034 · HTML: https://mlsanity.com/paper/2305.07034/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation](https://mlsanity.com/paper/1804.09849/) (2018)
- [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017)
- [Light Gated Recurrent Units for Speech Recognition](https://mlsanity.com/paper/1803.10225/) (2018)
- [Espresso: A Fast End-to-end Neural Speech Recognition Toolkit](https://mlsanity.com/paper/1909.08723/) (2019)
- [Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.02720/) (2017)
- [Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/1907.04224/) (2019)
- [Arabic Speech Recognition by End-to-End, Modular Systems and Human](https://mlsanity.com/paper/2101.08454/) (2021)
- [On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network](https://mlsanity.com/paper/1803.01686/) (2018)
- [Smartajweed Automatic Recognition of Arabic Quranic Recitation Rules](https://mlsanity.com/paper/2101.04200/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition](https://mlsanity.com/paper/2606.19747/) (2026)
- [AraS2P: Arabic Speech-to-Phonemes System](https://mlsanity.com/paper/2509.23504/) (2025)
- [A Critical Review of the Need for Knowledge-Centric Evaluation of Quranic Recitation](https://mlsanity.com/paper/2510.12858/) (2025)

## Abstract (arXiv)

The Quran is the holy scripture of Islam, and its recitation is an important aspect of the religion. Recognizing the recitation of the Holy Quran automatically is a challenging task due to its unique rules that are not applied in normal speaking speeches. A lot of research has been done in this domain, but previous works have detected recitation errors as a classification task or used traditional automatic speech recognition (ASR). In this paper, we proposed a novel end-to-end deep learning model for recognizing the recitation of the Holy Quran. The proposed model is a CNN-Bidirectional GRU encoder that uses CTC as an objective function, and a character-based decoder which is a beam search decoder. Moreover, all previous works were done on small private datasets consisting of short verses and a few chapters of the Holy Quran. As a result of using private datasets, no comparisons were done. To overcome this issue, we used a public dataset that has recently been published (Ar-DAD) and contains about 37 chapters that were recited by 30 reciters, with different recitation speeds and different types of pronunciation rules. The proposed model performance was evaluated using the most common evaluation metrics in speech recognition, word error rate (WER), and character error rate (CER). The results were 8.34% WER and 2.42% CER. We hope this research will be a baseline for comparisons with future research on this public new dataset (Ar-DAD).