Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
The Application of a Convolution Neural Network on Face and License Plate Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Chinese License Plate Recognition Using a Convolutional Neural Network
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 01
Performance and Scalability of GPU-Based Convolutional Neural Networks
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
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This paper presents machine-printed character recognition acquired from license plate using convolutional neural network (CNN). CNN is a special type of feed-forward multilayer perceptron trained in supervised mode using a gradient descent Backpropagation learning algorithm that enables automated feature extraction. Common methods usually apply a combination of handcrafted feature extractor and trainable classifier. This may result in sub-optimal result and low accuracy. CNN has proved to achieve state-of-the-art results in such tasks such as optical character recognition, generic objects recognition, real-time face detection and pose estimation, speech recognition, license plate recognition etc. CNN combines three architectural concept namely local receptive field, shared weights and subsampling. The combination of these concepts and optimization method resulted in accuracy around 98%. In this paper, the method implemented to increase the performance of character recognition using CNN is proposed and discussed.