Augmentation on CNNs for Handwritten Digit Classification in a Small Training Sample Size Situation
Yoshihiro Mitani, Y Fujita, Yoshihiko Hamamoto · Journal of Physics Conference Series · 2021
Abstract In general, a deep learning needs a lot of samples. However, in a practical pattern recognition problem, the number of training samples is usually limited. We investigate the effect of an image data augmentation by a perspective transformation on a convolution neural network(CNN) for handwritten digit classification in a small training sample size situation. The experimental results show the effectiveness of the image data augmentation by the perspective transformation on the CNN for handwritten digit classification particularly in the small training sample size situation.