Very Deep Neural Network for Handwritten Digit Recognition

Handwritten digit recognition is an important but challenging task. However, how to build an efficient artificial neural network architecture that can match human performance on the task of recognition of handwritten digit is still a difficult problem. In this paper, we proposed a new very deep neural network architecture for handwritten digit recognition. What is remarkable is that we did not depart from the classical convolutional neural networks architecture, but pushed it to the limit by substantially increasing the depth. By a carefully crafted design, we proposed two different basic building block and increase the depth of the network while keeping the computational budget constant. On the very competitive MNIST handwriting benchmark, our method achieve the best error rate ever reported on the original dataset (0.47%+/- 0.05 %), without data distortion or model combination, demonstrating the superiority of our work.