A biologically inspired system for fast handwritten digit recognition

Inspired by information processing of complex cells in visual cortex, we present a simple system for fast and robust feature extraction. Our method includes an unsupervised algorithm for learning invariant descriptors from data, and an architecture for the task of digit recognition. The proposed algorithm is not only efficient that training can be accomplished in a few iterations, but can map test data into invariant representations directly, in contrast to most existing generative model, which must perform inference by minimizing energy functions. The simulation results on the well known MNIST database show that these learnt descriptors demonstrate a clear topography with similar properties of complex cells, and extract features that are invariant to minor variations of input data. Recognition experiments also show that the learnt invariant feature descriptors improve the accuracy than classical feature descriptors and yield comparable classification results.