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Traditional approaches to Named Entity Recognition almost heavily rely on feature engineering. In this paper, we introduce a kind of bidirectional recurrent neural network with long short memory (BLSTM) to capture bidirectional and long dependencies in a sentence without any feature set. Our model combines BLSTM network with Conditional Random Field (CRF) layer to jointly decode the best output. Additionally,...
Automatic license plate recognition is an important form in the automatic target recognition. In recent years, there has a lot of research in license plate recognition, and many license plate recognition algorithms have been proposed and used. In this paper, a new license plate recognition approach is put forward based on the Radial Basis Function Neural Networks (RBFNN). Also discussed are the problem...
Neural network is a technology for intelligent transportation system and it is important in vehicle type recognition. However, traditional vehicle type recognition method always utilize BP network. A new vehicle type recognition based on radial basic function neural network was proposed. Also discussed are the problem of feature of vehicle feature vector, the problem of normalization of the image-size,...
The face recognition is an active subject in the area of computer pattern recognition, which has been a focus in reach for the last couple of decades because of its widely potential applications. A face recognition approach is put forward based on the RBF neural network. Also discussed are the problem of feature of a face image vector, the problem of normalization of the image-size, and the problem...
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