Using Neural Natural Network for Image Recognition in Bioinformatics

Using Neural Natural Network for Image Recognition in Bioinformatics

Dina Kharicheva (Moscow Pedagogical State University, Moscow, Russian Federation)
Copyright: © 2019 |Pages: 7
DOI: 10.4018/IJARB.2019070103

Abstract

Automatic image recognition is very useful in bioinformatics. This article presents a novel technique to recognize the characters in the number plate automatically by using connected component analysis (CCA), artificial neural network (ANN) and neural natural network (Triple N). The preprocessing steps, Sobel edge detection technique and CCA are applied to the captured image of the vehicle to obtain character images. ANN technique can be used over these images to recognize the characters of the image in bioinformatics. The preprocessing steps are used to remove the noise and to enhance the image for recognizing the characters effectively. After performing the preprocessing steps, the edge detection technique and CCA are carried out to separate the character images from the whole image which can be recognized using ANN. These text characters can be compared with database to find authentication of vehicle, identifying the owner of the vehicle, penalty bill generation, etc.
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Preprocessing

Preprocessing steps helps to obtain the license image region and characters in the license image effectively. Preprocessing operations are applied to an input image which may be a low resolution one containing noise components also. The preprocessing steps are adaptive histogram equalization and linearization method which are used to reduce the noise components and to enhance the image. Adaptive histogram equalization increases the global contrast of image and also improves the low-level contrast pixels. Median filters are used to remove the noise components of the image. It needs to convert vehicle image into digital image which needs finalization method in order to effectively analyze the number image. In digital image the vehicle image pixels are represented in terms of 0’s and 1’s. So, license image and characters are obtained in terms of 0’s and 1’s. Shows the input image of the vehicle which may contains tiny noise components and sometimes the resolution is very less which needs to apply the median filters and adaptive histogram equalization to the input image. The resultant image is shown in literature. This enhanced image is converted into digital image which is shown in lit for effective analysis.

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