Quranic Script Optical Text Recognition Using Deep Learning in IoT Systems

Badry, Mahmoud; Hassanin, Mohammed; Chandio, Asghar Ali; Moustafa, Nour

doi:10.32604/cmc.2021.015489

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Despite the potential benefits of mobile applications, challenges remain in ensuring their effective integration into Quran education. Badry et al (2021) and address the challenge of Quranic script recognition using deep learning in IoT systems, highlighting the importance of robust technological solutions. Mustaffa et al (2019) identify areas for improvement in Quranic Arabic vocabulary learning mobile applications through user reviews, emphasizing the need for continuous refinement and user feedback.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Adolescent views on digital Quran applications highlight the value of digitalization in making religious texts more accessible (Fajrie et al, 2023). Additionally, Quranic script optical text recognition using deep learning in IoT systems shows promise for automating Quranic text analysis (Badry et al, 2021). Determining areas of improvement in Quranic Arabic vocabulary learning mobile applications through user reviews is essential for refining educational tools (Mustaffa et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction

Hakimi,

Mursal AKRAMI,

Maliha AHRARI

et al. 2024

J. Dig. Learning and Dist. Edu

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Mobile applications have emerged as innovative tools in Quranic education, presenting opportunities to enhance learning experiences and engage students effectively. This study aims to assess the impact of mobile applications on Quranic education through a comprehensive survey-based investigation. The research method involved designing a structured questionnaire to gather data on various dimensions, including application usage patterns, effectiveness ratings, contributions to educational improvement, perceived impacts on students' understanding and retention of Quranic knowledge, enhancement of student performance, and comparisons between traditional and mobile application-based learning methods. The population for this study consisted of students enrolled in online university programs with a focus on Quranic education, with a sample size of 60 respondents contributing valuable insights. Quantitative data analysis encompassed descriptive statistics and inferential methods, enabling a thorough examination of survey responses and relationships between variables. The findings reveal diverse usage patterns of Quranic education applications, with respondents attributing significant value to mobile applications in enhancing Quranic education. Positive perceptions regarding the effectiveness of mobile applications in improving student performance and understanding of Quranic knowledge were prevalent among participants. Additionally, a clear preference for mobile application-based learning emerged over traditional methods. The study underscores the importance of leveraging mobile technology to enrich Quranic education and highlights avenues for future research in this domain. Overall, the research contributes to advancing knowledge on the role of mobile applications in Quranic education and informs pedagogical practices in digital learning environments.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction

Hakimi,

Mursal AKRAMI,

Maliha AHRARI

et al. 2024

J. Dig. Learning and Dist. Edu

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“…The data generated by IoT devices such as the Quran Read Pen, which helps to read Quran specifically to illiterate or blind people [48], is shared via the Quranic Text Image Dataset (QTID). It contains 309,720 images of words and 2,494,428 characters taken from the Quran, which uses the sequence-to-sequence model and CNN and achieves a high recognition rate.…”

Section: Line or Word Imagementioning

confidence: 99%

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

et al. 2023

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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.

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“…Their accuracy is determined by their classifying abilities. Reference [13] presents the architecture of the Quran-seq2seq and Quran-Full-CNN models. These are the combination of CNN and LSTM networks.…”

Section: Implicit Segmentationmentioning

confidence: 99%

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

Tayyab¹,

Hussain²,

Mir³

et al. 2023

Computers, Materials &Amp; Continua

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The news ticker is a common feature of many different news networks that display headlines and other information. News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities. In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel. The primary emphasis of this research is on ticker recognition methods and storage schemes. To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method. The proposed learning architecture considers the grouping of homogeneousshaped classes. This incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual biases. Furthermore, experiments with a novel Arabic News Ticker (Al-ENT) dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested approach. The proposed method attains 96.5%, outperforming the current state-of-the-art technique by 8.5%. The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

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Quranic Script Optical Text Recognition Using Deep Learning in IoT Systems

Cited by 8 publications

References 6 publications

The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction

The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction

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

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

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