A modular neural network classifier for the recognition of occluded characters in automatic license plate reading

被引：1
|
作者：
Nijhuis, JAG [1 ]
Broersma, A [1 ]
Spaanenburg, L [1 ]
机构：
[1] Univ Groningen, Dept Comp Sci, NL-9700 AV Groningen, Netherlands
来源：
COMPUTATIONAL INTELLIGENT SYSTEMS FOR APPLIED RESEARCH | 2002年
关键词：
D O I：
10.1142/9789812777102_0044
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Occlusion is the most common reason for lowered recognition yield in free-flow license-plate reading systems. (Non-)occluded characters can readily be learned in separate neural networks but not together. Even a small proportion of occluded characters in the training set will already significantly reduce the overall recognition yield. This paper shows that a modular network can handle a realistic mixture of (non-) occluded characters with a 99.8% recognition yield per character.
引用
收藏
页码：363 / 372
页数：10
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