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NIPS
1993
8 years 10 months ago
Postal Address Block Location Using a Convolutional Locator Network
This paper describes the use of a convolutional neural network to perform address block location on machine-printed mail pieces. Locating the address block is a dicult object rec...
Ralph Wolf, John C. Platt
NIPS
1997
8 years 10 months ago
A Neural Network Based Head Tracking System
We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector a...
Daniel D. Lee, H. Sebastian Seung
ESANN
2006
8 years 10 months ago
Visual object classification by sparse convolutional neural networks
Abstract. A convolutional network architecture termed sparse convolutional neural network (SCNN) is proposed and tested on a real-world classification task (car classification). In...
Alexander Gepperth
ICPR
2010
IEEE
9 years 1 months ago
Learning Sparse Face Features : Application to Face Verification
We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference per subject image. In cl...
Pierre Buyssens, Marinette Revenu
ICDAR
2003
IEEE
9 years 2 months ago
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods th...
Patrice Simard, David Steinkraus, John C. Platt
ICDAR
2005
IEEE
9 years 2 months ago
Text Recognition of Low-resolution Document Images
Cheap and versatile cameras make it possible to easily and quickly capture a wide variety of documents. However, low resolution cameras present a challenge to OCR because it is vi...
Charles E. Jacobs, Patrice Y. Simard, Paul A. Viol...
CVPR
1997
IEEE
9 years 11 months ago
Global Training of Document Processing Systems Using Graph Transformer Networks
We propose a new machine learning paradigm called Graph Transformer Networks that extends the applicability of gradient-based learning algorithms to systems composed of modules th...
Léon Bottou, Yoshua Bengio, Yann LeCun
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