Abstract
In recent years, text detection technology has advanced significantly. However, research on text detection of engineering drawings is lacking. The challenges faced by engineering drawing text detection are the degradation of partial occlusion and adhesion within texts, as well as the complex background noise. To address this problem, we propose an end-to-end text detection framework for degraded drawings based on multiscale feature fusion and instance segmentation, which adopts pluggable and stackable multiscale feature fusion modules to enhance the accuracy of the degraded text. We conduct experiments on several benchmarks to demonstrate the effectiveness of the proposed method on degraded drawing text and natural scene text.
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Wang, H., Shan, H., Song, Y., Meng, Y., Wu, M. (2023). Engineering Drawing Text Detection via Better Feature Fusion. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_23
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DOI: https://doi.org/10.1007/978-3-031-36819-6_23
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