Machine learning for handprinted character perception

Presents a functional model of machine learning by means of inductive inference to read handprinted text. This model can be used to characterise, compare and act as a generic model for methods of machine learning. The environment determines the design of the learning system. The proposed model of machine learning and perception of handwritten text has achieved character error rates comparable to those commonly experienced by humans when reading isolated handprinted characters