Arabic natural language processing for Qur’anic research: a systematic review

Bashir, Muhammad Huzaifa; Azmi, Aqil M.; Nawaz, Haq; Zaghouani, Wajdi; Diab, Mona; Al‐Fuqaha, Ala; Qadir, Junaid

doi:10.1007/s10462-022-10313-2

Cited by 19 publications

(13 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arabic, one of the world's most ancient and rich languages, boasts a script that holds a unique position in the tapestry of global written languages. It is not just a means of communication but a symbol of cultural heritage, religious significance, and historical depth [4].…”

Section: A Arabic Script and Arabic Languagementioning

confidence: 99%

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

Alshahrani,

Alghamdi

2024

ijacsa

View full text Add to dashboard Cite

The recognition of Arabic words presents considerable difficulties owing to the complex characteristics of the Arabic script, which encompasses letters positioned both above and below the baseline, hamzas, and dots. In order to address these intricacies, we provide a structured approach for transforming handwritten Arabic text into a digital format. We employ a hybrid deep learning technique that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BLSTM), and Connectionist Temporal Classification (CTC). We collected datasets that cover a wide range of Arabic text variations. We have also created a pre-processing pipeline. Our methodology successfully achieved an accuracy rate of 99.52%. At the level of recognizing the letters of the word, with an accuracy of 98.36% at the level of the full word. In order to evaluate the effectiveness of our suggested method for recognizing handwritten text, we utilize two essential metrics: Word Error Rate (WER) and Character Error Rate (CER) to compare its performance. The experimental research demonstrates a WER of 1.64 % and a CER of 0.48%.

show abstract

Section: A Arabic Script and Arabic Languagementioning

confidence: 99%

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

Alshahrani,

Alghamdi

2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…As Quran is a Holy Book, it is recited worldwide, and everyone wants to recite it correctly without any mistakes. Bashir et al [51] review the Quranic NLP techniques, approaches used, tools, and datasets, and recitation via speech-recognition method. The techniques used in the paper are text preprocessing, text matching, clustering, classification, and speech processing.…”

Section: Quranic Textmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

View full text Add to dashboard Cite

Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

show abstract

“…In [9], the authors presented a systematic review on Arabic NLP for Qur'anic research works (used tools, available data sets, proposed approaches) covering the wide spectrum from doing morphological analysis automatically to Qur'anic recitation correction using speech recognition.…”

Section: Natural Language Processingmentioning

confidence: 99%

Analysis of the Arab Singer Shadia’s Lyrics

Khamis

Gomaa

2023

Preprint

View full text Add to dashboard Cite

In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the twentieth century, namely, \<شادية> (Shadia). Lyrics analysis provides a deep insight into the artist's career evolution, her interactions with the surrounding environment including the social, political, and economic conditions. In order to perform such analysis we had to collect and compile the lyrics of Shadia accompanied with the necessary metadata into an organized and structured form. The data are preprocessed by removing stop words and doing some normalization operations over the songs prose. We did not perform any lemmatization or stemming as the original form of the tokens convey much more information than the source words.We performed a lexical analysis in order to study both the lexical density and diversity over the course of Shadia career life. We have as well studied the most significant words, idioms, and terms played in the songs using tools such as word clouds and more quantitative measures such as term frequency-inverse document frequency. We have divided the career life of Shadia into sub-decades of length \(5\) years and all analyses are done both in a yearly fashion and more coarsely over such sub-decades. We have found a strong correlation between our statistical analysis and the socio-political status in Egypt and the Arab world during that time.This is especially relevant knowing that Shadia is very much truly considered the daughter of the generation of the 1952 revolution in Egypt. The significance of Shadia and her lyrics stem essentially from being contemporaneous to radical changes in Egypt across all sectors including political (support of liberation movements across the world, and the conflict with Israel), and socio-economic (especially changing the social class structure in Egypt). We also investigated the potential effectiveness of PoS (Part of Speech) tagging in genre analysis and classification.

show abstract

Arabic natural language processing for Qur’anic research: a systematic review

Cited by 19 publications

References 55 publications

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

Analysis of the Arab Singer Shadia’s Lyrics

Contact Info

Product

Resources

About