General Applications

Neural network is utilized to convert text to speech, for natural language processing, for illustration, for deriving language rules, recognition of handwriting and characters, pattern recognition and image processing. This line of research has instantaneous employ in credit card processing, banking processing and other financial services where recognizing and reading handwriting on documents is crucial. Neural network using neocognitron can distinguish handwritten numerals of different styles of penmanship correctly, though they are considerably distorted in shape. Neural networks can be trained to recognize or identify alphabets of other set of patterns as like: Arabic numerals, geometric shapes or others. A pattern can be observes through a visual system and recognized properly via the multilayered hierarchical network by utilizing self-organization. The recognition of hand written patterns via a machine is immensely complex task; conversely, neural networks along with its vast potential have attracted the researchers in the region of machine perception via presenting an exciting, complementary option to symbolic processing paradigms. The technique of neocognitron is utilized to recognize handwritten alphanumeric characters.

This neural network can attain the capability to recognize patterns by learning, and could be trained to recognize any set of patterns, this has also a large power of generalization, and representation of merely a few typical illustrations of deformed patterns is sufficient for the learning. These are utilized also in regions like pattern recognition, image recognition and optical character recognition.