Handwritten digit recognition by means of a holographic associative memory

Abstract

In this paper, a holographic associative memory (HAM) is proposed for recognizing handwritten variations of the ten digits. First, the handwritten characters were taken from the NIST standard database in order to extract relevant features from each one of them. Each digit was thus represented as a vector of 112 features constructed by dividing each character into 16 equal-sized partitions, each one used to extract seven different features for recognition. Second, these feature vectors, and reduced combinations of them, were input to train several HAM systems respectively. Then, all these memories were tested with a new set of patterns and the lowest-error HAM was chosen as the best training set. The features used in this last memory were taken as the most significant variables for describing each digit in the database. Finally, these most significant features were used to show the behaviour of the recognition rate when training the HAM with reduced training sets. Some final conclusions are reported and future work directions are proposed.