Single Document Image Highlight Removal via A Large-Scale Real-World Dataset and A Location-Aware Network
This work addresses a domain-specific problem for document image processing by providing a dedicated dataset and method to improve highlight removal, though it is incremental as it builds on existing deep learning approaches with tailored designs.
The paper tackles the problem of specular highlights degrading text readability in reflective document images by introducing DocHR14K, a large-scale real-world dataset with 14,902 image pairs, and L2HRNet, a location-aware network that achieves state-of-the-art performance, including a 5.01% increase in PSNR and 13.17% reduction in RMSE on their dataset.
Reflective documents often suffer from specular highlights under ambient lighting, severely hindering text readability and degrading overall visual quality. Although recent deep learning methods show promise in highlight removal, they remain suboptimal for document images, primarily due to the lack of dedicated datasets and tailored architectural designs. To tackle these challenges, we present DocHR14K, a large-scale real-world dataset comprising 14,902 high-resolution image pairs across six document categories and various lighting conditions. To the best of our knowledge, this is the first high-resolution dataset for document highlight removal that captures a wide range of real-world lighting conditions. Additionally, motivated by the observation that the residual map between highlighted and clean images naturally reveals the spatial structure of highlight regions, we propose a simple yet effective Highlight Location Prior (HLP) to estimate highlight masks without human annotations. Building on this prior, we present the Location-Aware Laplacian Pyramid Highlight Removal Network (L2HRNet), which effectively removes highlights by leveraging estimated priors and incorporates diffusion module to restore details. Extensive experiments demonstrate that DocHR14K improves highlight removal under diverse lighting conditions. Our L2HRNet achieves state-of-the-art performance across three benchmark datasets, including a 5.01\% increase in PSNR and a 13.17\% reduction in RMSE on DocHR14K.