Handwritten digit recognition using biologically inspired features

Image recognition problems are usually difficult to solve using raw pixel data. To improve the recognition it is often needed some form of feature extraction to represent the data in a feature space. We use the output of a biologically inspired model for visual recognition as a feature space. The output of the model is a binary code which is used to train a linear classifier for recognizing handwritten digits using the MNIST and USPS datasets. We evaluate the robustness of the approach to a variable number of training samples and compare its performance on these popular datasets to other published results. We achieve competitive error rates on both datasets while greatly improving relatively to related networks using a linear classifier.