Shadow Removal of Text Document Images Using Background Estimation and Adaptive Text Enhancement
원문 초록
This paper proposes a simple yet effective method to re-move shadows from text document images. It mainly includes several parts. Firstly, we propose a text elimination-based background extraction strategy to estimate shadow map. It indicates the shadow regions accurately and helps to predict global background. Secondly, a binarization-based text ex-traction algorithm is designed to obtain texts from document image. By fusing texts and global background, a preparatory shadow-free image can be obtained. Thirdly, we propose an adaptive text contrast enhancement strategy to generate shadow-free results with comfortable visual perception across shadow and non-shadow regions. Quantitative and visual results performed on open datasets indicate that the proposed method can generate clear shadow-free images from text document images. Our code will be publicly available soon.
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High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net
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OmniSR: Shadow Removal under Direct and Indirect Lighting
SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer
토론
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