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This paper presents a multilayered artificial neural network model to recognize numerals independent of their sizes, positions, and orientations. We used a modified backpropagation algorithm to train the network. We preprocessed the numerals for "feature" extraction by calculating their moments. The moments are invariant under translation, scaling, and rotation transformations. We used the moments as input to the network rather than the digitized pattern itself. The network was able to recognize transformed and slightly deformed numerals. Parallel computational capability of the network makes it an attractive alternative for real-time commercial applications.

Transformations and distortions tolerant recognition of numerals using neural networks