3 research outputs found
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.Comment: 10 pages, 8 figure