JACIII Vol.23 No.2 pp. 261-267
doi: 10.20965/jaciii.2019.p0261


A Sketch Recognition Algorithm Based on Bayesian Network and Convolution Neural Network

Xiang Hou

School of Intelligent Manufacturing, Sichuan University of Arts and Science
No.519 Tashi Road, Dazhou, Sichuan 635000, China

May 27, 2018
July 24, 2018
March 20, 2019
Bayesian network, stroke grouping, convolution neural network, sketch recognition

Most of the existing sketch recognition algorithms are used to restrict the user’s drawing habits to achieve the stroke grouping and recognition. In order to solve the problem, a new sketch recognition algorithm based on Bayesian network and convolution neural network (CNN) is proposed. First, the input sketch is processed by Gaussian low-pass filter and a smoother stroke can be obtained. The stroke of continuous input is divided, then the Bayesian network and CNN are performed on stroke recognition respectively. The recognition result of Bayesian network is adopted when the reliability of stroke is larger than the threshold, otherwise recognition result of CNN will be adopted. The experiment result shows that the proposed algorithm is effective in circuit symbol recognition. The recognition rate was achieved 80.34% in the drawing process, and the final recognition rate was achieved 93.48%.

The accurate crowd counting statistics in scenic spots

The accurate crowd counting statistics in scenic spots

Cite this article as:
X. Hou, “A Sketch Recognition Algorithm Based on Bayesian Network and Convolution Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.2, pp. 261-267, 2019.
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Last updated on Jul. 12, 2024