Multi-Digit Recognition Using a Space Displacement Neural Network

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

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Ofer Matan, Christopher J. C. Burges, Yann LeCun, John Denker


We present a feed-forward network architecture for recognizing an uncon(cid:173) strained handwritten multi-digit string. This is an extension of previous work on recognizing isolated digits. In this architecture a single digit rec(cid:173) ognizer is replicated over the input. The output layer of the network is coupled to a Viterbi alignment module that chooses the best interpretation of the input. Training errors are propagated through the Viterbi module. The novelty in this procedure is that segmentation is done on the feature maps developed in the Space Displacement Neural Network (SDNN) rather than the input (pixel) space.