A Deep Learning Approach for Recognizing the Noon Rule for Reciting Holy Quran

Abstract

Ahkam Al-Tajweed represents the most precious religious heritage that is in critical need to be preserved and kept for the next generation. This study tackles the challenge of learning Ahkam Al-Tajweed by developing a model that considers one of the rules experienced by early learners in the Holy Quran. The proposed model focuses, specifically, on the "Hukm Al-Noon Al-Mushaddah," which pertains to the proper pronunciation of the letter "Noon" when it is accompanied by a Shaddah symbol in Arabic. By incorporating this rule into the proposed model, learners will benefit the model because it will improve their Tajweed skills and facilitate the learning process for those who do not have access to private tutors or experts. The proposed approach involved three models namely, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Random Forest models in the context of a classification task. The models were evaluated based on their validation accuracy, and the results indicate that the CNN model achieved the highest validation accuracy of 0.8613. The other contribution of this work is collecting a novel dataset for this kind of study. The findings show that the Random Forest model outperformed the other models in terms of accuracy.