We propose a new machine learning paradigm called Multilayer Graph Transformer Network that extends the applicability of gradient-based learning algorithms to systems composed of modules that take graphs as input and produce graphs as output. A complete check reading system based on this concept is described. The system combines convolutional neural network character recognizers with graph-based stochastic models trained cooperatively at the document level. It is deployed commercially and reads million of business and personal checks per month with record accuracy.
CITATION STYLE
Le Cun, Y., Bottou, L., & Bengio, Y. (1997). Reading checks with multilayer graph transformer networks. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 1, pp. 151–154). IEEE. https://doi.org/10.1109/icassp.1997.599580
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