This week Ian is going to talk about his ICLR submission.
The room is going to be the main Lisa Lab (AA 3256).
Hope to see many of you there.
Title: Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Abstract: A tea talk on this ICLR submission:
Recognizing arbitrary multi-character text in
unconstrained natural photographs is a hard problem. In this paper, we
address an equally hard sub-problem in this domain viz. recognizing
arbitrary multi-digit numbers from Street View imagery. Traditional
approaches to solve this problem typically separate out the
localization, segmentation, and recognition steps. In this paper we
propose a unified approach that integrates these three steps via the use
of a deep convolutional neural network that operates directly on the
image pixels. We employ the DistBelief (Dean et al., 2012)
implementation of deep neural networks in order to train large,
distributed neural networks on high quality images. We find that the
performance of this approach increases with the depth of the
convolutional network, with the best performance occurring in the
deepest architecture we trained, with eleven hidden layers. We evaluate
this approach on the publicly available SVHN dataset and achieve over
96% accuracy in recognizing complete street numbers. We show that on a
per-digit recognition task, we improve upon the state-of-the-art and
achieve 97.84% accuracy. We also evaluate this approach on an even
more challenging dataset generated from Street View imagery containing
several tens of millions of street number annotations and achieve over
90% accuracy. Our evaluations further indicate that at specific operating
thresholds, the performance of the proposed system is comparable to
that of human operators. To date, our system has helped us extract close
to 100 million physical street numbers from Street View imagery
worldwide.
Best,