Ncfm: Accurate handwritten digits recognition using Convolutional Neural Networks

Abstract

Convolutional Neural Networks(CNNs) have been confirmed as a powerful technique for classification of visual inputs like handwritten digits and faces recognition. Traditional convolutional layer's input feature maps are convolved with learnable kernel then combined for achieving better performance. The biggest drawback is that the combination of feature maps can lose features and do not apply well to large-scale neural networks. In this paper, we introduce Ncfm(No combination of feature maps), a novel technique to improve the performance of CNNs. By applying Ncfm technique, the input feature maps are not combined, which implies that the number of input feature maps is equal to output feature maps. The Ncfm technique converges faster and performs better than Cfm (Combination of feature maps) with fewer filters. Through the type of feature map, experimental evaluation shows that the performance is improved and we achieve the state-of-the-art performance with 99.81% accuracy rate on the MNIST datasets.