@Article{cmc.2023.034669,
AUTHOR = {Moeen Tayyab, Ayyaz Hussain, Usama Mir, M. Aqeel Iqbal, Muhammad Haneef},
TITLE = {Visual News Ticker Surveillance Approach from Arabic Broadcast Streams},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {3},
PAGES = {6177--6193},
URL = {http://www.techscience.com/cmc/v74n3/50952},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.034669}
}