Recognition of character using morphological associative memory

This work provides a research in pattern recognition using a hardware implemented classifier based on morphological associative memory (MAM). Most neural networks (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 character from roman's alphabet shows the versatility of this architecture.