This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.

Central to many formulations of sequence recognition are problems in sequential decision-making. Typically, a sequence of events is observed through a transformation that introduces uncertainty into the observations, and based on these observations, the recognition process produces a hypothesis of the underlying events. The events in the underlying process are constrained to follow a certain loose order, for example by a grammar, so that decisions made early in the recognition process restrict or narrow the choices that can be made later. This problem is well known and leads to the use of dynamic programming (DP) algorithms [Bel57] so that unalterable decisions can be avoided until all available information has been processed. DP strategies are central to hidden Markov model (HMM) recognizers [LMS84,Lev85,Rab89,RBH86] and have also been widely used in systems based on neural networks (e.g., [SIY 89,Bur88,BW89,SL92,BM90,FLW90]) to transform static pattern classifiers into sequence recognizers.

This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.

This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.

This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.