Open Access iconOpen Access

ARTICLE

crossmark

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

Moeen Tayyab1, Ayyaz Hussain2,*, Usama Mir3, M. Aqeel Iqbal4, Muhammad Haneef5

1 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
2 Department of Computer Science, Quaid-i-Azam University, Islamabad, 44000, Pakistan
3 Department of Computer Science, Senior Member IEEE, University of Windsor, N9B 3P4, Canada
4 Department of Software Engineering, Foundation University Islamabad, Islamabad, 44000, Pakistan
5 Department of Electrical Engineering, Foundation University Islamabad, Islamabad, 44000, Pakistan

* Corresponding Author: Ayyaz Hussain. Email: email

Computers, Materials & Continua 2023, 74(3), 6177-6193. https://doi.org/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 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 low-representation correlated character classes.

Keywords


Cite This Article

APA Style
Tayyab, M., Hussain, A., Mir, U., Iqbal, M.A., Haneef, M. (2023). Visual news ticker surveillance approach from arabic broadcast streams. Computers, Materials & Continua, 74(3), 6177-6193. https://doi.org/10.32604/cmc.2023.034669
Vancouver Style
Tayyab M, Hussain A, Mir U, Iqbal MA, Haneef M. Visual news ticker surveillance approach from arabic broadcast streams. Comput Mater Contin. 2023;74(3):6177-6193 https://doi.org/10.32604/cmc.2023.034669
IEEE Style
M. Tayyab, A. Hussain, U. Mir, M.A. Iqbal, and M. Haneef "Visual News Ticker Surveillance Approach from Arabic Broadcast Streams," Comput. Mater. Contin., vol. 74, no. 3, pp. 6177-6193. 2023. https://doi.org/10.32604/cmc.2023.034669



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 827

    View

  • 376

    Download

  • 0

    Like

Share Link