Journal ArticleDOI
A neural network approach to character recognition
TLDR
The sensitivity of the network is such that small variations in the input do not affect the output and this results in an improvement in the recognition rate of characters with slight variations in structure, linearity, and orientation.About:
This article is published in Neural Networks.The article was published on 1989-07-01. It has received 128 citations till now. The article focuses on the topics: Intelligent character recognition & Time delay neural network.read more
Citations
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Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
Growing and pruning neural tree networks
A. Sakar,Richard J. Mammone +1 more
TL;DR: It is shown that this method has better performance in terms of minimizing the number of classification errors than the squared error minimization method used in backpropagation.
Journal ArticleDOI
Location of subsurface targets in geophysical data using neural networks
TL;DR: Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target.
Journal ArticleDOI
Recognition of handwritten cursive Arabic characters
TL;DR: An automatic off-line character recognition system for handwritten cursive Arabic characters is presented and proved to be powerful in tolerance to variable writing, speed, and recognition rate.
Journal ArticleDOI
Hand-printed arabic character recognition system using an artificial network
TL;DR: This approach has a number of advantages: it combines rule-based (structural) and classification tests; it is more efficient for large and complex sets such as Arabic characters; feature extraction is inexpensive and the execution time is independent of character font and size.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Proceedings Article
Training feedforward neural networks using genetic algorithms
David J. Montana,Lawrence Davis +1 more
TL;DR: A set of experiments performed on data from a sonar image classification problem are described to illustrate the improvements gained by using a genetic algorithm rather than backpropagation and chronicle the evolution of the performance of the genetic algorithm as it added more and more domain-specific knowledge into it.
Journal ArticleDOI
A Closed Set of Normal Orthogonal Functions
TL;DR: In this article, the authors studied a new closed set of functions normal and orthogonal on the interval (0, 1) for the interval 0 5 x 5 1, where each function takes only the values + 1 and − 1, except at a finite number of points of discontinuity, where it takes the value zero.
Journal ArticleDOI
On the Recognition of Printed Characters of Any Font and Size
TL;DR: The current state of a system that recognizes printed text of various fonts and sizes for the Roman alphabet is described, which combines several techniques in order to improve the overall recognition rate.