Global training of document processing systems using graph transformer networks

Leon Bottou, Yoshua Bengio, Yann Le Cun

Research output: Contribution to journalConference article

Abstract

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 that take graphs as inputs and produce graphs as output. Training is performed by computing gradients of a global objective function with respect to all the parameters in the system using a kind of back-propagation procedure. A complete check reading system based on these concepts is described. The system uses convolutional neural network character recognizers, combined with global training techniques to provide record accuracy on business and personal checks. It is presently deployed commercially and reads millions of checks per month.

Original languageEnglish (US)
Pages (from-to)489-494
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 1997
EventProceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - San Juan, PR, USA
Duration: Jun 17 1997Jun 19 1997

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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