Badriyya B. Al-onazi¹, Najm Alotaibi², Jaber S. Alzahrani³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Radwa Marzouk⁵, Mahmoud Othman⁶, Abdelwahed Motwakel^7,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1537-1554, 2023, DOI:10.32604/cmc.2023.034196

Abstract Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes. When the number of labels is limited to one, the task becomes single-label text categorization. The Arabic texts include unstructured information also like English texts, and that is understandable for machine learning (ML) techniques, the text is changed and demonstrated by numerical value. In recent times, the dominant method for natural language processing (NLP) tasks is recurrent neural network (RNN), in general, long short term memory (LSTM) and convolutional neural network (CNN). Deep learning (DL) models are currently presented for deriving… More >

Moeen Tayyab¹, Ayyaz Hussain^2,*, Usama Mir³, M. Aqeel Iqbal⁴, Muhammad Haneef⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6177-6193, 2023, DOI:10.32604/cmc.2023.034669

Abstract 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 homogeneous-shaped classes. This incorporates linguistic taxonomy in… More >

Hicham Lamtougui^1,*, Hicham El Moubtahij², Hassan Fouadi¹, Khalid Satori¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2871-2888, 2023, DOI:10.32604/cmc.2023.032550

Abstract In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a… More >

Fatmah Baothman^*, Sarah Alssagaff, Bayan Ashmeel

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 519-531, 2021, DOI:10.32604/iasc.2021.014828

Abstract The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative… More >