| Summary: | 碩士 === 國立高雄第一科技大學 === 電腦與通訊工程所 === 94 === Abstract
In this thesis, we propose two methods to recognize container code in container image .One is to do the recognition of container code by combining the Neural Network with the digital image processing, the other is the method combines that the Rule Base with the digital image processing to do the recognition of container code. In this thesis, the neural network used to recognize container code is Back-Propagation Neural Network. Because front four characters of container code are English and post seven characters are Number. we divide the neural network into English part and Number part. The rule base approach is also divided into English rule base and Number rule base.
Image processing techniques involving in the proposed methods are noise elimination, container code location, container code segmentation. The feature vectors used here are the White Run-Length Code and Pixel Density. In finial experiment, we use container code of 120 pictures to test recognition rate. As a result, the recognition rates of two method are both more than 85%. Recognition rate of Neural network is 86.6% and recognition rate of knowledge Rule Base is 87.5%.
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