An Industrial Scene Text Detection with Spectral Domain Enhancement and Graph Fourier Mapping
Wocheng Xiao, Lingyu Liang, Shuangping Huang · 2024
Text detection is a task of great significance in different scenarios, which has wide applications for downstream tasks such as text recognition and text retrieval. Varieties of text detection methods have been proposed to solve this problem in natural scenes and have achieved good results. However, these methods cannot get satisfied performance in industrial scenes for various interferences caused by the industrial environment like the image noise, background material texture and low contrast. To deal with these challenges, we first propose a contour modeling algorithm based on graph Fourier transform mapping to represent arbitrary shaped text contours. A refined fast Fourier convolutional network module with ability of spectral-domain sensing is also intruduced to enhance text feature and suppress interference. Based on these two components, we construct a novel network to achieve accurate industrial scene text detection. Quantitative evaluations are conducted on benchmark datasets MPSC and IcText, experimental results show that our method obtains the state-of-the-art detection accuracy.