Automatic Image and Speech Recognition Based on Neural Network

2010 ◽  
Vol 3 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Dariusz Król ◽  
Boguslaw Szlachetko
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Recognition System ◽  
License Plate ◽  
Network Technology ◽  
License Plate Recognition ◽  
Neural Structure ◽  
Automatic Speech Recognition System ◽  
System A ◽  
Neural Network Technology

The objective of this paper is to present a real-time mechanism for recognition of different objects using Spatiognitron neural network technology. Spatiognitron is based on a biological neural structure and the theory described in this paper presents what are known as Time Delay Neural Networks (TDNN). These are fields which enable the recognition of different features in the input object. The approach was verified by qualitative recognition process tests in commercial car license plate recognition using a NeuroCar based system. A second set of tests was carried out in a laboratory environment using NeuroScope, an automatic speech recognition system.

Automatic Car Number Plate Recognition System for Authorization

2017 ◽  
Vol MCSP2017 (01) ◽  
pp. 30-34
Author(s):  
Somalin Sandha ◽  
Debaraj Rana
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Performance Level ◽  
License Plate ◽  
License Plate Recognition ◽  
Human Power ◽  
Automatic License Plate Recognition

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

Convolution Neural Network Based Automatic License Plate Recognition System

2019 ◽  
Vol 7 (4) ◽  
pp. 199-205
Author(s):  
Aman Raj ◽  
Devanshu Dubey ◽  
Abhishek Mishra ◽  
Nikhil Chopda ◽  
Nishant M. Borkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Convolution Neural Network ◽  
License Plate ◽  
License Plate Recognition ◽  
Automatic License Plate Recognition

Extraction of License Plate Region in License Plate Recognition System

2013 ◽  
Vol 441 ◽  
pp. 655-659
Author(s):  
Yuan Ning ◽  
Yao Wen Liu ◽  
Yan Bin Zhang ◽  
Hao Yuan
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Recognition System ◽  
Intelligent Vehicle ◽  
License Plate ◽  
License Plate Recognition ◽  
Important Stage ◽  
System A ◽  
Average Accuracy ◽  
Extraction Algorithm

Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.

scholarly journals A design of license plate recognition system using convolutional neural network

2019 ◽  
Vol 9 (3) ◽  
pp. 2196
Author(s):  
P. Marzuki ◽  
A. R. Syafeeza ◽  
Y. C. Wong ◽  
N. A. Hamid ◽  
A. Nur Alisa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Recognition System ◽  
Network Size ◽  
License Plate ◽  
License Plate Recognition ◽  
Sobel Operator ◽  
Proposed Model ◽  
Best Parameters

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

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