6,258 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
Defense against Universal Adversarial Perturbations
Recent advances in Deep Learning show the existence of image-agnostic
quasi-imperceptible perturbations that when applied to `any' image can fool a
state-of-the-art network classifier to change its prediction about the image
label. These `Universal Adversarial Perturbations' pose a serious threat to the
success of Deep Learning in practice. We present the first dedicated framework
to effectively defend the networks against such perturbations. Our approach
learns a Perturbation Rectifying Network (PRN) as `pre-input' layers to a
targeted model, such that the targeted model needs no modification. The PRN is
learned from real and synthetic image-agnostic perturbations, where an
efficient method to compute the latter is also proposed. A perturbation
detector is separately trained on the Discrete Cosine Transform of the
input-output difference of the PRN. A query image is first passed through the
PRN and verified by the detector. If a perturbation is detected, the output of
the PRN is used for label prediction instead of the actual image. A rigorous
evaluation shows that our framework can defend the network classifiers against
unseen adversarial perturbations in the real-world scenarios with up to 97.5%
success rate. The PRN also generalizes well in the sense that training for one
targeted network defends another network with a comparable success rate.Comment: Accepted in IEEE CVPR 201
AON: Towards Arbitrarily-Oriented Text Recognition
Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted)
arrangements, which have not yet been well addressed in the literature.
Existing methods on text recognition mainly work with regular (horizontal and
frontal) texts and cannot be trivially generalized to handle irregular texts.
In this paper, we develop the arbitrary orientation network (AON) to directly
capture the deep features of irregular texts, which are combined into an
attention-based decoder to generate character sequence. The whole network can
be trained end-to-end by using only images and word-level annotations.
Extensive experiments on various benchmarks, including the CUTE80,
SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed
AON-based method achieves the-state-of-the-art performance in irregular
datasets, and is comparable to major existing methods in regular datasets.Comment: Accepted by CVPR201
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