Visual News Ticker Surveillance Approach from Arabic Broadcast Streams  

作　　者：Moeen Tayyab Ayyaz Hussain Usama Mir M.Aqeel Iqbal Muhammad Haneef 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第3期6177-6193,共17页计算机、材料和连续体（英文）

摘　　要：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 homogeneousshaped 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 ArabicNews 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 lowrepresentation correlated character classes.

关 键 词：Arabic text recognition optical character recognition deep convolutional network SegNet LeNet 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]