Recognition of character using morphological associative memory

The article provides a research in pattern recognition using a hardware implemented classifier based on morphological associative memory (MAM). Most neural network (NN) models used for this purpose have a complex learning rule. Instead, we propose a morphological classifier that has a simple learning rule based on mathematical morphology and presents an infinity storage capacity. Initially, we define a standard for the image as pre-requirement for pre-processing. The pre-processing stage is performed to reduce the dimensionality of MAM´s data input in order to act as an interface between the inputs and the classifier. A practical application on recognizing characters from the Roman alphabet shows the versatility of this architecture