Offline Handwritten Chinese Character Recognition Based on DBN and CNN Fusion Model - LI Lanying; ZHOU Zhigang; CHEN Deyun (Journal of Harbin University of Science and Technology)
Aiming at the problem that some offline handwritten Chinese characters are similar in shape and it is difficult to extract the feature of characters and the recognition is not accurate, a convolutional neural network and deep belief network fusion model is proposed Firstly, the convolutional neural network and the deep belief network are trained on the dataset respectively It is found that the comprehensive TOP-2 accuracy of the both can reach 99.33% Using the advantages of convolutional neural networks and deep belief networks in image analysis, a fusion comparison strategy is adopted to extract a classification result as accurately as possible in the TOP-2 classification of the two to improve the recognition ability The experimental results show that the fusion model of convolutional neural network and deep belief network has better recognition effect than convolutional neural network and deep belief network