The Common Fold: Utilizing the Four-Fold to Dewarp Printed Documents from a Single Image

被引:23
作者
Das, Sagnik [1 ]
Mishra, Gaurav [1 ]
Sudharshana, Akshay [1 ]
Shilkrot, Roy [1 ]
机构
[1] SUNY Stony Brook, Stony Brook, NY 11794 USA
来源
PROCEEDINGS OF THE 2017 ACM SYMPOSIUM ON DOCUMENT ENGINEERING (DOCENG 17) | 2017年
关键词
Folded Document Dewarping; Document Reconstruction; Document Image Processing; 3D SHAPE; RESTORATION; RECONSTRUCTION; SURFACE;
D O I
10.1145/3103010.3121030
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Handheld cameras are currently the device of choice for performing document digitization, due to their convenience, ubiquity and high performance at low cost. Software methods process a captured image, to rectify distortions and reconstruct the original document. Existing methods struggle to reconstruct a flattened version given a single image of a document distorted by folding. We propose a novel non-parametric page dewarping approach from a single image based on deep learning to identify creases due to folds on the paper. Our method then performs a 2D boundary method based on polynomial regression, and a Coons patch, to get a flattened reconstruction. We found our method improves OCR word accuracy by more than 2 : 5 times when compared to the original distorted image.
引用
收藏
页码:125 / 128
页数:4
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