A modular classification scheme with elastic net models for handwritten digit recognition

This paper describes a modular class$cation system for handwritten digit recognition based on the elastic net model. We use ten separate elastic nets to capture different features in the ten classes of handwritten digits and represent an input sample from the activations in each net by population decoding. Compared with traditional neural networks based discriminant classifiers, our scheme features fast training and high recognition accuracy.