Graphic Symbol Recognition of Engineering Drawings Based on Multi-Scale Autoconvolution Transform

Authors:
Chuan-Min Zhai;Ji-Xiang Du
Affiliations:
Department of Computer Science and Technology, Huaqiao University, China;Department of Computer Science and Technology, Huaqiao University, China and Department of Automation, University of Science and Technology of China,
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In this paper, a novel graphic symbol recognition of scanned engineering drawing method based on multi-scale autoconvolution transform and radial basis probabilistic neural network (RBPNN) is proposed. Firstly, the recently proposed affine invariant image transform called Multi-Scale Autoconvolution (MSA) is adopted to extract invariant features. Then, the orthogonal least square algorithm (OLSA) is used to train the RBPNN and the recursive OLSA is adopted to optimize the structure of the RBPNN. The experimental result shows that, compared with another affine invariant technique, this new method provides a good basis for the scanned engineering drawing recognition task where the disturbances of graphic symbol can be approximated with spatial affine transformation.