Recovering Homography from Camera Captured Documents using Convolutional Neural Networks

[wanghaisheng]

Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Removing perspective distortion from hand held camera
captured document images is one of the primitive tasks in
document analysis, but unfortunately no such method exists
that can reliably remove the perspective distortion from
document images automatically. In this paper, we propose a
convolutional neural network based method for recovering
homography from hand-held camera captured documents.
Our proposed method works independent of document’s
underlying content and is trained end-to-end in a fully automatic
way. Specifically, this paper makes following three
contributions: firstly, we introduce a large scale synthetic
dataset for recovering homography from documents images
captured under different geometric and photometric transformations;
secondly, we show that a generic convolutional
neural network based architecture can be successfully used
for regressing the corners positions of documents captured
under wild settings; thirdly, we show that L1 loss can be reliably
used for corners regression. Our proposed method
gives state-of-the-art performance on the tested datasets,
and has potential to become an integral part of document
analysis pipeline.
1709.03524.pdf
https://arxiv.org/pdf/1709.03524.pdf