Proceedings ArticleDOI
Self-organizing integrated segmentation and recognition neural network
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TLDR
A neural network algorithm that simultaneously performs segmentation and recognition of input patterns that self-organizes to detect input pattern locations and pattern boundaries and simultaneously segments and recognizes touching characters, overlapping characters, broken characters, and noisy images with high accuracy is presented.Abstract:
We present a neural network algorithm that simultaneously performs segmentation and recognition of input patterns that self-organizes to detect input pattern locations and pattern boundaries. We outline the algorithm and demonstrate this neural network architecture and algorithm on character recognition using the NIST database and report results herein. The resulting system simultaneously segments and recognizes touching characters, overlapping characters, broken characters, and noisy images with high accuracy. We also detail some of the characteristics of the algorithm on an artificial database in the appendix.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.read more
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References
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Journal ArticleDOI
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
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A time-delay neural network architecture for isolated word recognition
TL;DR: A translation-invariant back-propagation network is described that performs better than a sophisticated continuous acoustic parameter hidden Markov model on a noisy, 100-speaker confusable vocabulary isolated word recognition task.
Proceedings Article
Integrated Segmentation and Recognition of Hand-Printed Numerals
TL;DR: A neural network algorithm that simultaneously segments and recognizes in an integrated system that uses a supervised learning algorithm (backpropagation), but is able to take position-independent information as targets and self-organize the activities of the units in a competitive fashion to infer the positional information.
Proceedings Article
Recognizing Hand-Printed Letters and Digits
Gale L. Martin,James A. Pittman +1 more
TL;DR: It is suggested that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy, and doubts about the relevance to backpropagation of learning models that estimate the likelihood of high generalization from estimates of capacity are raised.
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