Abstract
In hand-drawn sketch recognition, the traditional deep learning method has the problems of insufficient feature extraction and low recognition rate. To solve this problem, a new algorithm based on a dual-channel convolutional neural network is proposed. Firstly, the sketch is preprocessed to get a smooth sketch. The contour of the sketch is obtained by the contour extraction algorithm. Then, the sketch and contour are used as the input image of CNN. Finally, feature fusion is carried out in the full connection layer, and the classification results are obtained by using a softmax classifier. Experimental results show that this method can effectively improve the recognition rate of a hand-drawn sketch.
Highlights
As a very important field of hospital cultural image design, popular science publicity can better promote the construction of hospital culture
3.2 Experimental results and analysis Deep learning requires a large amount of training data, and the lack of training data tends to create an over-fit problem
4 Conclusion and future work In order to improve the recognition rate of a hand-drawn sketch recognition, a handdrawn sketch recognition algorithm based on a dual-channel convolution neural network is proposed
Summary
As a very important field of hospital cultural image design, popular science publicity can better promote the construction of hospital culture. The high-quality hand-painted popular science images reflect the spread of high-quality popular science. The application of this method in the recognition of medical hand-painted popular science sketches can better spread health knowledge to the public. 2.1 Convolution neural network CNN is an algorithm with less human intervention. The traditional BP neural network is used for reference in the process of weight updating. Due to the lack of human intervention, CNN can directly take the image as input and automatically extract image features for recognition. The weight sharing and local sensing characteristics of CNN reduce the number of parameters in the network, and work in a way similar to that of animal visual nerve cells. The recognition accuracy and efficiency of the network are greatly improved
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