Postal Address Block Location Using a Convolutional Locator Network

By Ralph Wolf, John C. Platt. 1993

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Shape from a Low Number of Silhouettes (2016, background): “Also, fully convolutional networks prove their eﬃciency in one dimensional input strings [12] extended from LeNet [13], two dimensional detection [14,15] with learning and inference, three dimensional representation [16], volumetric 3D shapes classiﬁcation and interpolation [17].”
A Convolutional Neural Network Hand Tracker (1994, methodology): “Convolutional networks have been successfullyapplied to machine print OCR (Platt et al, 1992), machine print address blocklocation (Wolf and Platt, 1994), and hand printed OCR (Le Cun et al, 1990; Martinand Rashid, 1992).”
STFCN: Spatio-Temporal FCN for Semantic Video Segmentation (2016, methodology): “On the way of its development, it has been used for 1-dimensional (1D) and 2-dimensional (2D) inputs [34,44], and for solving various tasks such as image restoration, sliding window detection, depth estimation, boundary prediction, or semantic segmentation.”
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping (2015, methodology): “Fully convolutional models have been used successfully for spatial analysis of arbitrary size inputs (Wolf and Platt 1994; Long, Shelhamer, and Darrell 2014).”
Stochastic Learning (2003, background): “Readers interested by the practice of stochastic gradient algorithms should also read [25] and investigate applied contributions such as [39, 37, 46, 6, 24, 26].”
Disentangling Factors of Variation for Facial Expression Recognition (2012, background): “Convolutional neural networks are generalizations of neural networks which have been particularly successful in computer vision [12–16].”
Object Recognition with Gradient-Based Learning (1999, methodology): “The idea has been applied to face location, [30], address block location on envelopes [ 33 ], and hand tracking in video [24].”
PixelNet: Towards a General Pixel-level Architecture (2016, methodology): “Neural networks with convolutional output predictions, also called Fully Convolutional Networks (FCNs) [13, 48, 51, 57], appear to be a promising architecture in this direction.”