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Neural Network based Road Sign Recognition

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 10
Year of Publication: 2012
Sanjit Kumar Saha
Dulal Chakraborty
Md. Al-amin Bhuiyan

Sanjit Kumar Saha, Dulal Chakraborty and Md. Al-amin Bhuiyan. Article: Neural Network based Road Sign Recognition. International Journal of Computer Applications 50(10):35-41, July 2012. Full text available. BibTeX

	author = {Sanjit Kumar Saha and Dulal Chakraborty and Md. Al-amin Bhuiyan},
	title = {Article: Neural Network based Road Sign Recognition},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {10},
	pages = {35-41},
	month = {July},
	note = {Full text available}


A recent surge of interest is to recognize Road Signs. Signs are visual languages that represent some special circumstantial information of environment. They provide important information for guiding, warning people to make their movements safer, easier and more convenient. This thesis presents a hybrid neural network solution for Road sign recognition which combines local image sampling and artificial neural network. The method is based on BAM for dimensional reduction and multi-layer perception with backpropagation algorithm has been used for training the network. It has been found from practical observations that the number of iterations required to train the network is enormous. The capability of recognition of a neural network increases with increasing the training accuracy. For this process each sign is converted to a designated M×N feature matrix. These feature matrices of signs are then fed into the neural network as input patterns. The neural network is trained with the set of input patterns of the digits to acquire separate knowledge corresponding to each Road sign. In order to justify the effectiveness of the system, different test patterns of the signs are used to verify the system. Experimental results demonstrate that the system is capable of recognizing Road signs with 98% accuracy.


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