A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition

TL;DR

This paper introduces a Saak transform-based method for handwritten digits recognition that is scalable, efficient, and robust, offering an alternative to traditional CNN approaches like LeNet-5.

Contribution

It proposes a novel multi-stage Saak transform approach with PCA-based kernel selection for improved recognition performance and efficiency.

Findings

Saak transform achieves comparable accuracy to CNNs.

Lossy Saak transform reduces feature size with minimal accuracy loss.

Method demonstrates enhanced scalability and robustness.

Abstract

An efficient, scalable and robust approach to the handwritten digits recognition problem based on the Saak transform is proposed in this work. First, multi-stage Saak transforms are used to extract a family of joint spatial-spectral representations of input images. Then, the Saak coefficients are used as features and fed into the SVM classifier for the classification task. In order to control the size of Saak coefficients, we adopt a lossy Saak transform that uses the principal component analysis (PCA) to select a smaller set of transform kernels. The handwritten digits recognition problem is well solved by the convolutional neural network (CNN) such as the LeNet-5. We conduct a comparative study on the performance of the LeNet-5 and the Saak-transform-based solutions in terms of scalability and robustness as well as the efficiency of lossless and lossy Saak transforms under a comparable accuracy level.