DocumentCode :
174205
Title :
A robust approach to the recognition of text based Bangla road sign
Author :
Banik, Bithi ; Alam, Fahim Irfan
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Chittagong, Chittagong, Bangladesh
fYear :
2014
fDate :
23-24 May 2014
Firstpage :
1
Lastpage :
7
Abstract :
Road sign recognition is considered to be one of the most fascinating and interesting field of research in intelligent vehicle and machine learning. Road signs are typically placed either by the roadside or above roads. They provide important information in order to make driving safer and easier. This paper proposes an algorithm that recognizes Bangla road sign with a better percentage. The algorithm starts with capture image from real video scene, text detection from images, character segmentation and recognition of characters through shape matrix. The constructed feature vectors for each individual Bangla road sign are learned into a neural network which later classifies new instance of Bangla road sign. The promising preliminary experimental results indicate a positive potential of our algorithm.
Keywords :
driver information systems; feature extraction; image classification; image segmentation; matrix algebra; natural language processing; optical character recognition; road traffic; text analysis; vectors; video signal processing; character recognition; character segmentation; feature vectors; intelligent vehicle; machine learning; neural network; real video scene; shape matrix; text based Bangla road sign recognition; text detection; Character recognition; Feature extraction; Image edge detection; Image segmentation; Roads; Shape; Text recognition; Bangla road sign recognition; bag of words; character recognition; character segmentation; text detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Informatics, Electronics & Vision (ICIEV), 2014 International Conference on
Conference_Location :
Dhaka
Print_ISBN :
978-1-4799-5179-6
Type :
conf
DOI :
10.1109/ICIEV.2014.6850852
Filename :
6850852
Link To Document :
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