We explore the use of certain image features, blockwise histograms of local orientations, used in many current object recognition algorithms, for the task of handwritten digit recognition. Existing approaches find that polynomial kernel SVMs trained on raw pixels achieve state of the art performance. However such kernel SVM approaches are impractical as they have a huge complexity at runtime. We demonstrate that with improved features a low complexity classifier, in particular an additive-kernel SVM, can achieve state of the art performance. Our approach achieves an error of 0.79% on the MNIST dataset and 3.4% error on the USPS dataset, while running at speeds comparable to the fastest algorithms on these datasets which are based on multilayer neural networks and are significantly faster and easier to train.