عند تحليل التهجئة في خط النسخ والرقعة والدواني والفارسي والدواني جلي GPT دقة نظام التعرف الضوئي على الحروف في دردشة

Farika Riskiyah

Abstract


تعلم اللغة العربية قد شهد تحولًا كبيرًا من النماذج الكلاسيكية إلى النماذج الرقمية. هذا التغيير مدفوع بالتقدم في مجالات اللغويات والتعليم والتكنولوجيا. أحد هذه التقدمات هو ظهور الذكاء الاصطناعي، الذي غالبًا ما يساعد في أعمال البشر، خاصة في مجال التعلم. أحد نماذج الذكاء الاصطناعي المستخدمة في مجال التعلم هو دردشة GPT .دردشة GPT هو نموذج ذكاء اصطناعي لديه إمكانات كبيرة في تعزيز تجربة التعلم للمتعلمين باللغة العربية. يستخدم هذا النموذج تكنولوجيا معالجة اللغة الطبيعية (NLP) لفهم واستجابة اللغة البشرية، وتقديم ملاحظات فورية وتصحيحات وشروحات. أحد الأنظمة الموجودة في دردشة GPT هو نظام التعرف الضوئي على الحروف (OCR) الذي يقوم بمسح الصور وتحويلها إلى نصوص. تهدف هذه المقالة إلى قياس مستوى دقة نظام التعرف الضوئي على الحروف في دردشة GPT في تهجئة الأحرف العربية المستمدة من أنواع مختلفة من الخطوط، وهي خط النسخ، الرقعة، الديواني، الفارسي، والديواني جلي. تستخدم هذه المقالة نهجًا نوعيًا بتصميم دراسة مكتبية. من المتوقع أن تقدم النتائج معلومات للقراء حول كيفية عمل نظام التعرف الضوئي على الحروف ودقته في معالجة تهجئة النصوص العربية.


Full Text:

PDF

References


Abad-Segura, E., González-Zamar, M.-D., Infante-Moro, J. C., & Ruipérez García, G. (2020). Sustainable Management of Digital Transformation in Higher Education: Global Research Trends. Sustainability, 12(5), 2107. https://doi.org/10.3390/su12052107

Abdelhadi, R., Hameed, L., Khaled, F., & Anderson, J. (2020). Creative interactions with art works: an engaging approach to Arabic language-and-culture learning. Innovation in Language Learning and Teaching, 14(3), 273–289. https://doi.org/10.1080/17501229.2019.1579219

Abou Adel, Mohammed A. (2022). Investing Artificial Intelligence For Arabic Learning/ استثمار الذكاء الاصطناعي في تعلم اللغة العربية. Ijaz Arabi Journal of Arabic Learning, 5(1). https://doi.org/10.18860/ijazarabi.v5i1.15814

Adriano, J. E. M., Calma, K. A. S., Lopez, N. T., Parado, J. A., Rabago, L. W., & Cabardo, J. M. (2019). Digital conversion model for hand-filled forms using optical character recognition (OCR). IOP Conference Series: Materials Science and Engineering, 482, 012049. https://doi.org/10.1088/1757-899X/482/1/012049

Agrawal, P., Chaudhary, D., Madaan, V., Zabrovskiy, A., Prodan, R., Kimovski, D., & Timmerer, C. (2021). Automated bank cheque verification using image processing and deep learning methods. Multimedia Tools and Applications, 80(4), 5319–5350. https://doi.org/10.1007/s11042-020-09818-1

Alghyaline, S. (2022). Real-time Jordanian license plate recognition using deep learning. Journal of King Saud University - Computer and Information Sciences, 34(6), 2601–2609. https://doi.org/10.1016/j.jksuci.2020.09.018

Alghyaline, S. (2023). Arabic Optical Character Recognition: A Review. Computer Modeling in Engineering & Sciences, 135(3), 1825–1861. https://doi.org/10.32604/cmes.2022.024555

Alyafeai, Z., Al-shaibani, M. S., Ghaleb, M., & Al-Wajih, Y. A. (2022). Calliar: an online handwritten dataset for Arabic calligraphy. Neural Computing and Applications, 34(23), 20701–20713. https://doi.org/10.1007/s00521-022-07537-2

Ashiq, V. M., & Fredrik, E. J. T. (2023). AN OCR FOR ARABIC CHARACTERS WITH ADVANCED BASELINE SEGMENTATION AND ENHANCED CONVOLUTIONAL NEURAL NETWORK FOR CLASSIFICATION. Journal of Theoretical and Applied Information Technology, 101(17), 7044–7055. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174942538&partnerID=40&md5=73a6b963009684d69865ca298ea63dba

Bassam, R., & Samann, F. (2020). Smart Parking System based on Improved OCR Model. IOP Conference Series: Materials Science and Engineering, 978(1), 012007. https://doi.org/10.1088/1757-899X/978/1/012007

Chen, K., Shao, A., Burapacheep, J., & Li, Y. (2024). Conversational AI and equity through assessing GPT-3’s communication with diverse social groups on contentious topics. Scientific Reports, 14(1), 1561. https://doi.org/10.1038/s41598-024-51969-w

Faizullah, S., Ayub, M. S., Hussain, S., & Khan, M. A. (2023). A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges. Applied Sciences, 13(7), 4584. https://doi.org/10.3390/app13074584

Garg, D. S., & Chauhan, D. A. (2024). Chat GPT-4: Potentials, barriers, and future directions for newer medical researchers. The American Journal of the Medical Sciences, 367(6), 406–408. https://doi.org/10.1016/j.amjms.2024.03.016

Gürer, D. Z., & Gökbay, İ. Z. (2023). Arabic Calligraphy Image Analysis with Using Traditional Machine Learning Algorithms and Transfer Learning. 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–7. https://doi.org/10.1109/ASYU58738.2023.10296629

Gürer, D. Z., & Gökbay, İ. Z. (2024). Arabic Calligraphy Image Analysis With Transfer Learning. ELECTRICA, 24(1), 201–209. https://doi.org/10.5152/electrica.2023.23102

Jafari, E. (2024). Artificial intelligence and learning environment: Human considerations. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.13011

Leunard, H., Rachmawati, R., Zani, B. N., & Maharjan, K. (2023). GPT Chat: Opportunities and Challenges in the Learning Process of Arabic Language in Higher Education. Journal International of Lingua and Technology, 2(1), 10–22. https://doi.org/10.55849/jiltech.v2i1.439

Lowe, M., Prusa, J. D., Leevy, J. L., & Khoshgoftaar, T. M. (2024). Advancing machine learning with OCR2SEQ: an innovative approach to multi-modal data augmentation. Journal of Big Data, 11(1), 86. https://doi.org/10.1186/s40537-024-00927-4

Mohsen, A. A., Abdul Amir, W. K., & Al-Nouri, A. A. Y. (2023). Artistic Signature’s Aesthetic Consistency and Differentiation in Arabic Calligraphy. Journal of Islamic Thought and Civilization, 13(2), 70–85. https://doi.org/10.32350/jitc.132.05

Nahar, K. M. O., Alsmadi, I., Al Mamlook, R. E., Nasayreh, A., Gharaibeh, H., Almuflih, A. S., & Alasim, F. (2023). Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques. Sensors, 23(23), 9475. https://doi.org/10.3390/s23239475

Najam, R., & Faizullah, S. (2023). Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction. Applied Sciences, 13(13), 7568. https://doi.org/10.3390/app13137568

Pettersson, T., Riveiro, M., & Löfström, T. (2024). Multimodal fine-grained grocery product recognition using image and OCR text. Machine Vision and Applications, 35(4), 79. https://doi.org/10.1007/s00138-024-01549-9

Rahmati, M., Fateh, M., Rezvani, M., Tajary, A., & Abolghasemi, V. (2020). Printed Persian OCR system using deep learning. IET Image Processing, 14(15), 3920–3931. https://doi.org/10.1049/iet-ipr.2019.0728

Rini, R., Mustofa, A., & Kurniawan, R. (2022). Transformation Of Arabic Learning From Classical Model To Digital Model. Ijaz Arabi Journal of Arabic Learning, 5(3). https://doi.org/10.18860/ijazarabi.v5i3.17409

Rolls, E. T. (2024). The memory systems of the human brain and generative artificial intelligence. Heliyon, 10(11), e31965. https://doi.org/10.1016/j.heliyon.2024.e31965

Rouabhi, S., Azerine, A., Tlemsani, R., Essaid, M., & Idoumghar, L. (2024). Conv-ViT fusion for improved handwritten Arabic character classification. Signal, Image and Video Processing, 18(S1), 355–372. https://doi.org/10.1007/s11760-024-03158-5

Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097

Sulaeman, I. S. S. S. I. (2023). ChatGPT as a New Frontier in Arabic Education Technology. Al-Arabi: Jurnal Bahasa Arab Dan Pengajarannya = Al-Arabi: Journal of Teaching Arabic as a Foreign Language, Vol 7, No 1 (2023): Al-Arabi: Journal of Teaching Arabic as a Foreign Language, 83–105. http://journal2.um.ac.id/index.php/alarabi/article/view/36954/11797

Takano, A., Cole, T. C. H., & Konagai, H. (2024). A novel automated label data extraction and data base generation system from herbarium specimen images using OCR and NER. Scientific Reports, 14(1), 112. https://doi.org/10.1038/s41598-023-50179-0

Talaat, A., Suen, C., Refaat, M. M., & Sallam, A. (2014). A Proposed OCR Algorithm for the Recognition of Handwritten Arabic Characters. Journal of Pattern Recognition and Intelligent Systems, 2, 90–104.

Yamina, O. J., Mamoun, M. El, & Kaddour, S. (2017). Printed Arabic optical character recognition using support vector machine. 2017 International Conference on Mathematics and Information Technology (ICMIT), 134–140. https://doi.org/10.1109/MATHIT.2017.8259707

Zoizou, A., Errebiai, C., Zarghili, A., & Chaker, I. (2024). A study of feature extraction for Arabic calligraphy characters recognition. International Journal of Electrical and Computer Engineering (IJECE), 14(1), 870. https://doi.org/10.11591/ijece.v14i1.pp870-877

Zoizou, A., Zarghili, A., & Chaker, I. (2019). Skew Correction and Text Line Extraction of Arabic Historical Documents (pp. 181–193). https://doi.org/10.1007/978-3-030-32959-4_13


Refbacks

  • There are currently no refbacks.