@article{Tayyab2023, 
author = {Moeen Tayyab and Ayyaz Hussain and Usama Mir and M. Aqeel Iqbal and Muhammad Haneef},
title = {Visual News Ticker Surveillance Approach from Arabic Broadcast Streams},
year = {2023},
journal = {Computers, Materials & Continua},
volume = {74},
number = {3},
pages = {6177-6193},
keywords = {Arabic text recognition, optical character recognition, deep convolutional network, SegNet, LeNet},
url = {https://www.sciopen.com/article/10.32604/cmc.2023.034669},
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 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.}
}