用混合方法高效修复相机拍摄的文档扭曲，精度高且省资源。
Efficient Document Image Dewarping via Hybrid Deep Learning and Cubic Polynomial Geometry Restoration
- YOLOv8检测文档区域，再用多项式插值重建网格并校正畸变
- CER仅0.0235，接近扫描件质量，比现有方法更优
- 适合移动端或低算力设备，开源可复现
相机拍摄的文档图像常因纸张变形、透视畸变和镜头像差导致几何失真，严重影响OCR识别准确率。本文提出一种高效自动化文档去扭曲方法，兼顾精度与计算效率。结合深度学习与经典计算机视觉：YOLOv8完成初始文档分割与掩码生成；随后通过立方多项式插值构建拓扑二维网格，再进行图像重映射以纠正非线性畸变。我们发布了新标注数据集与开源框架，支持可复现与进一步研究。在主流方法（RectiNet、DocGeoNet、DocTr++）及移动应用（DocScan、CamScanner、TapScanner）对比中，本方法实现最低中位字符错误率（CER=0.0235）、莱文施泰因距离（LD=27.8），最高贾罗-温克勒相似度（JW=0.902），接近扫描原图质量。相比纯深度学习方案，本方法显著降低计算资源与内存占用，同时提升OCR可读性与几何还原质量。该混合方法在保持高精度的同时具备优异计算效率，适用于资源受限场景下的高质量文档数字化。项目页：https://github.com/HorizonParadox/DRCCBI
原文摘要 · Abstract (English)
Camera-captured document images often suffer from geometric distortions caused by paper deformation, perspective distortion, and lens aberrations, significantly reducing OCR accuracy. This study develops an efficient automated method for document image dewarping that balances accuracy with computational efficiency. We propose a hybrid approach combining deep learning for document detection with classical computer vision for geometry restoration. YOLOv8 performs initial document segmentation and mask generation. Subsequently, classical CV techniques construct a topological 2D grid through cubic polynomial interpolation of document boundaries, followed by image remapping to correct nonlinear distortions. A new annotated dataset and open-source framework are provided to facilitate reproducibility and further research. Experimental evaluation against state-of-the-art methods (RectiNet, DocGeoNet, DocTr++) and mobile applications (DocScan, CamScanner, TapScanner) demonstrates superior performance. Our method achieves the lowest median Character Error Rate (CER=0.0235), Levenshtein Distance (LD=27.8), and highest Jaro--Winkler similarity (JW=0.902), approaching the quality of scanned originals. The approach requires significantly fewer computational resources and memory compared to pure deep learning solutions while delivering better OCR readability and geometry restoration quality. The proposed hybrid methodology effectively restores document geometry with computational efficiency superior to existing deep learning approaches, making it suitable for resource-constrained applications while maintaining high-quality document digitization. Project page: https://github.com/HorizonParadox/DRCCBI
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。