Abstract
In this paper a new neural network is proposed for recognition of handwritten digits and multi-font machine printed characters. In this system, overlapped regional chain code histograms of characters are used as features and a neural network has been used for classification. A new neural network learning algorithm that combines unsupervised learning with supervised learning has been developed. This new algorithm overcomes the slow learning and difficult convergence problems that are typical of back-propagation learning algorithms. The algorithm was tested on a large set of handwritten digits collected from real world data and a set of multi-font machine printed English letters.
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