Title :
OCR-based neural network for ANPR
Author :
Zhai, Xiaojun ; Bensaali, Faycal ; Sotudeh, Reza
Author_Institution :
Sch. of Eng. & Technol., Univ. of Hertfordshire, Hatfield, UK
Abstract :
Optical Character Recognition (OCR) is the last stage in an Automatic Number Plate Recognition System (ANPRs). In this stage the number plate characters on the number plate image are converted into encoded texts. In this paper, an Artificial Neural Network (ANN) based OCR algorithm for ANPR application is presented. A database of 3700 UK binary character images have been used for testing the performance of the proposed algorithm. Results achieved have shown that the proposed algorithm can meet the real-time requirement of an ANPR system and can averagely process a character image in 8.4ms with 97.3% successful recognition rate.
Keywords :
neural nets; optical character recognition; text analysis; visual databases; ANN based OCR algorithm; ANPR system; OCR-based artificial neural network; UK binary character image database; automatic number plate recognition system; encoded texts; number plate characters; number plate image; optical character recognition; Biological neural networks; Character recognition; Field programmable gate arrays; Neurons; Optical character recognition software; Vectors; Automatic Number Plate Recognition; Neural Network; Optical Character Recognition;
Conference_Titel :
Imaging Systems and Techniques (IST), 2012 IEEE International Conference on
Conference_Location :
Manchester
Print_ISBN :
978-1-4577-1776-5
DOI :
10.1109/IST.2012.6295581
