文件名称：Multi-digit Number Recognition from Street View Imagery using DCNN
文件大小：4.74MB
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更新时间：2018-06-10 04:59:46
DeepLearning
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-theart, achieving 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. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to transcribing synthetic distorted text from a popular CAPTCHA service, reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text as one of the cues to distinguish humans from bots. With the proposed approach we report a 99.8% accuracy on transcribing the hardest category of reCAPTCHA puzzles. Our evaluations on both tasks, the street number recognition as well as reCAPTCHA puzzle transcription, indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.