Optimising Handwritten-Character Recognition with Logic Neural Networks
This article studies the implementation of a handwritten character recognition task using neural networks. Two logic neural network models axe employed to classify the Essex dataset, which comprises real-world hand-written characters. To reduce the underlying dataset variation, several pre-processing approaches are investigated. This allows the comparison of the network models on the basis of their classification accuracy for datasets with different characteristics.
KeywordsRecognition Rate Training Pattern Handwritten Character Binary Pixel Logic Neural Network
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