@inproceedings {348,
	title = {Robust hough-based symbol recognition using knowledge-based hierarchical neural networks},
	booktitle = {2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition},
	year = {2008},
	publisher = {CSREA Press},
	organization = {CSREA Press},
	abstract = {<p>A robust method for symbol recognition is presented that utilizes a compact signature based on a modified Hough Transform (HT) and knowledge-based hierarchical neural network structure. Relative position and orientation information is extracted from a symbol image using a modified Hough Transform (HT). This information is transformed and compressed into a compact, 1-D signature vector that is invariant to geometric transformations such as translation, rotation, scaling, and reflection. The proposed method uses a knowledge-based hierarchical neural network structure to reduce the complexity of the recognition process by effectively segmenting the search space into smaller and more manageable clusters based on a priori knowledge. The method achieved overall recognition rates of 96.7\% on line graphic symbols from the GREC\&rsquo;05 symbol database under various models of image degradation and distortion.</p>
},
	keywords = {hierarchical neural networks, hough transform, symbol recognition},
	isbn = {1-60132-078-7},
	author = {A Wong and W Bishop and H R. Arabnia}
}
