This research focuses on developing a method for restoring the topology of digital images of paper documents captured by a camera, using algorithms for detection, segmentation, geometry restoration, and dewarping. Our methodology employs deep learning (DL) for document outline detection, followed by computer vision (CV) to create a topological 2D grid using cubic polynomial interpolation and correct nonlinear distortions by remapping the image. Using classical CV methods makes the document topology restoration process more efficient and faster, as it requires significantly fewer computational resources and memory. We developed a new pipeline for automatic document dewarping and reconstruction, along with a framework and annotated dataset to demonstrate its efficiency. Our experiments confirm the promise of our methodology and its superiority over existing benchmarks (including mobile apps and popular DL solutions, such as RectiNet, DocGeoNet, and DocTr++) both visually and in terms of document readability via Optical Character Recognition (OCR) and geometry restoration metrics. This paves the way for creating high-quality digital copies of paper documents and enhancing the efficiency of OCR systems. Project page: https://github.com/HorizonParadox/DRCCBI

本研究解决了相机拍摄的纸质文档图像的几何恢复问题，采用深度学习算法进行文档轮廓检测，并利用计算机视觉技术构建拓扑二维网格，有效纠正非线性失真。研究结果表明，该方法在文档可读性和几何恢复指标上优于现有的基准解决方案，具有潜在的应用价值，能够提高纸质文档的数字化质量和OCR系统的效率。

相机捕获文档图像的几何恢复与去扭曲