[Lisa_teatalk] Tea talk tomorrow 13:00 (changed room to AA3256)

Razvan Pascanu r.pascanu at gmail.com
Tue Feb 25 18:42:39 EST 2014


Hi all,

>
>  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:
>
> https://drive.google.com/file/d/0B64011x02sIkd3RwSDRpTXlKSzQ/edit?usp=sharing
> 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,
> Razvan
>
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