Handwritten Yi Character Recognition with Density-Based Clustering Algorithm and Convolutional Neural Network
Xiaodong Jia, Wendong Gong, Yuan Jie · 2017
A great deal of research has focused on using convolutional neural network for optical character recognition. However we encountered two typical problem in this field when applied convolutional neural network to handwritten Yi character recognition. First, since convolutional neural network is a kind of supervised deep learning model, the manual training data labeling is a very time consuming and labor intensive work. Second, because the theory is not well studied, the structure design and parameter adjustment of convolutional neural network depend heavily on experience, and our recognition accuracy was not satisfactory at the beginning. To address these two problems, in this paper, for one thing, we use entropy theory improved a density-based clustering algorithm, which is proved very effective in data labeling. For another, as to the problem of structure design and parameter adjustment, we compared performance of models with different scales and different parameters, and gave some experience about this problem. Finally we achieved 99.65% accuracy on the test set. We hope that this paper will inspire more researches on convolutional neural network applied to dataset-lacked optical character recognition problems.