Character recognition from deblurred motion distorted Vehicle image using Neural Network

International Journal of Electronics and Communication Engineering

© 2014 by SSRG - IJECE Journal

Volume 1 Issue 4

Year of Publication : 2014

Authors : Ms. Dipalee A. Kolte , Prof. Maruti B. Limkar and Prof. Sanjay M. Hundiwale

10.14445/23488549/IJECE-V1I4P101

How to Cite?

Ms. Dipalee A. Kolte , Prof. Maruti B. Limkar and Prof. Sanjay M. Hundiwale, "Character recognition from deblurred motion distorted Vehicle image using Neural Network," SSRG International Journal of Electronics and Communication Engineering, vol. 1,  no. 4, pp. 1-9, 2014. Crossref, https://doi.org/10.14445/23488549/IJECE-V1I4P101

Abstract:

One of the severe hitches in Digital Imaging is to recuperate a deblurred image from a lone blurred image primarily when the blur category is motion. Here we put forward an approach to eradicate blurring a single image caused due to motion. Under certain appropriate redundant tight frame arrangement, the sparsity of both the blur kernel and original image are regularized by framing the blind blurring as an innovative combined optimization problem. In addition to this an adapted version of Split Bregman method is proposed to perform Blind Image deconvolution. Without demanding any former information of the blur kernel a deblurred vehicle image is obtained. Edge detection and morphological operation is implemented to extract the license plate from the deblurred vehicle image. The Characters from the extracted License Plate are segmented by Matrix scanning. The features of these segmented characters are learned by using Radon transform. Finally, neural network (NN) is commonly used to perform character recognition due to their great noise tolerance

Keywords:

Blind Image Deconvolution, Edge Detection, Morphological Operation, Motion Blur, Neural Network

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