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.

We present a feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string. This is an extension of previous work on recognizing isolated digits. In this architecture a single digit recognizer 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.

We present a feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string. This is an extension of previous work on recognizing isolated digits. In this architecture a single digit recognizer 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.

Ofer Matan*, Christopher J.C. Burges, Yann Le Cun and John S. Denker AT&T Bell Laboratories, Holmdel, N. J. 07733 Abstract We present a feed-forward network architecture for recognizing an unconstrained handwrittenmulti-digit string. This is an extension of previous work on recognizing isolated digits. 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. 1 Introduction In previous work (Le Cun et al., 1990) we have demonstrated a feed-forward backpropagation networkthat recognizes isolated handwritten digits at state-of-the-art performance levels.