BI DIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORK METHOD IN THE CHARACTER RECOGNITION
Yash Pal Singh, Amit Kumar Gupta, Abhilash Khare · 2009
Pattern recognition techniques are associated a symbolic identity with the image of the pattern. In this work we will analyze different neural network methods in pattern recognition. This problem of replication of patterns by machines (computers) involves the machine printed patterns. The pattern recognition is better known as optical pattern recognition. Since, it deals with recognition of optically processed patterns rather then magnetically processed ones. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. There is no idle memory containing data and programmed, but each neuron is programmed and continuously active. Neural network has many applications. The most likely applications for the neural networks are (1) Classification (2) Association and (3) Reasoning. One of the applications of neural networks is in the field of pattern recognition. The Bidirectional associative memory does heteroassociative processing in which, association between pattern pairs is stored. The Bidirectional Associative has capacity limitations. It can store and correctly recognize only six characters, with the condition that the characters should be slightly similar in shape.