Title
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks
Abstract
Capturing document images with hand-held devices in unstructured environments is a common practice nowadays. However, "casual" photos of documents are usually unsuitable for automatic information extraction, mainly due to physical distortion of the document paper, as well as various camera positions and illumination conditions. In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image. Our insight is that the 3D geometry of the document not only determines the warping of its texture but also causes the illumination effects. Therefore, our novelty resides on the explicit modeling of 3D shape for document paper in an end-to-end pipeline. Also, we contribute the largest and most comprehensive dataset for document image unwarping to date - Doc3D. This dataset features multiple ground-truth annotations, including 3D shape, surface normals, UV map, albedo image, etc. Training with Doc3D, we demonstrate state-of-the-art performance for DewarpNet with extensive qualitative and quantitative evaluations. Our network also significantly improves OCR performance on captured document images, decreasing character error rate by 42% on average. Both the code and the dataset are released.
Year
DOI
Venue
2019
10.1109/ICCV.2019.00022
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Keywords
Field
DocType
DewarpNet,single-image document unwarping,document image,captured document images,Doc3D,stacked 3D regression networks,stacked 2D regression networks,deep-learning approach,camera positions,illumination conditions,3D geometry,qualitative evaluation,quantitative evaluation
Computer vision,Regression,Pattern recognition,Computer science,Artificial intelligence
Conference
Volume
Issue
ISSN
2019
1
1550-5499
ISBN
Citations 
PageRank 
978-1-7281-4804-5
1
0.36
References 
Authors
26
5
Name
Order
Citations
PageRank
Sagnik Das142.44
Ke Ma29827.97
Zhixin Shu3135.26
Dimitris Samaras41740101.49
Roy Shilkrot516114.81