Di Zhou1, Jianxun Zhang1,*, Chao Li2, Yifan Guo1, Bowen Li1
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1821-1842, 2024, DOI:10.32604/cmc.2023.047236
Abstract Text perception is crucial for understanding the semantics of outdoor scenes, making it a key requirement for building intelligent systems for driver assistance or autonomous driving. Text information in car-mounted videos can assist drivers in making decisions. However, Car-mounted video text images pose challenges such as complex backgrounds, small fonts, and the need for real-time detection. We proposed a robust Car-mounted Video Text Detector (CVTD). It is a lightweight text detection model based on ResNet18 for feature extraction, capable of detecting text in arbitrary shapes. Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation… More >
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