TY - EJOU AU - Tayyab, Moeen AU - Hussain, Ayyaz AU - Mir, Usama AU - Iqbal, M. Aqeel AU - Haneef, Muhammad TI - Visual News Ticker Surveillance Approach from Arabic Broadcast Streams T2 - Computers, Materials \& Continua PY - 2023 VL - 74 IS - 3 SN - 1546-2226 AB - 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. KW - Arabic text recognition; optical character recognition; deep convolutional network; SegNet; LeNet DO - 10.32604/cmc.2023.034669