Comparative Analysis of Various Soft Computing Technique-Based Automatic Licence Plate Recognition Systems

Traffic on Indian roads is growing day by day leading to accidents. The intelligent transport system is the solution to resolve the traffic problem on roads. One of the components of the intelligent transportation system is the monitoring of traffic by the automatic licence plate recognition system. In this chapter, a automatic licence plate recognition systems based on soft computing techniques is presented. Images of Indian vehicle licence plates are used as the dataset. Firstly, the licence plate region is extracted from the captured image, and thereafter, the characters are segmented. Then features are extracted from the segmented characters which are used for the recognition purpose. Furthermore, artificial neural network, support vector machine, and convolutional neural network are used and compared for the automatic licence plate recognition. The future scope is the hybrid technique solution to the problem.

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A Hybrid Approach For Automatic Licence Plate Recognition System

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666200304131816 ◽

2020 ◽

Vol 10 ◽

Author(s):

Nitin Sharma ◽

Pawan Kumar Dahiya ◽

Baldev Raj Marwah

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Recognition System ◽

Traffic Monitoring ◽

Support Vector ◽

Network Support ◽

Recognition Systems ◽

Licence Plate Recognition

: Automatic licence plate recognition systems are used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. In this paper, a convolutional neural network and support vector machine based automatic licence plate recognition system is proposed. Firstly, The characters extracts from the input image of vehicle. Then characters are segment and their features are extracts. The extracted features are classified using convolutional neural network and support vector machine for the final recognition of the licence plate. The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The recognition rate obtained for the proposed hybrid automatic licence plate system are compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. The proposed automatic licence plate recognition system perform better than the neural network, support vector machine, and convolutional nerural network based automatic licence plate recognition systems.

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Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3344.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 470-474

Keyword(s):

Neural Network ◽

Soft Computing ◽

Traffic Management ◽

Support Vector ◽

Hybrid Technique ◽

Management Problem ◽

Computing Technique ◽

Soft Computing Techniques ◽

Recognition Systems ◽

Licence Plate Recognition

Automobile industries are growing exponentially in last decade in India. Growth in the vehicle numbers results in much more road accidents and traffic management problem. Not only this, long queues at toll plazas and parking lot is also a major issue of concern. Problem of traffic management and long queues can be solved by automatic licence plate recognition systems. In this paper, an automatic Licence Plate Recognition Systems based on soft computing techniques are presented. Indian vehicle with licence plates were used for testing the implemented systems. Firstly the licence plate image is extracted from the vehicle image and the characters are segmented from the extracted licence plate image and then features are extracted from the segmented characters which are used for the recognition. Soft computing techniques random forest, neural network, support vector machine, and convolutional neural network are used for the implementation pusrpose. The results obtained for the applied soft computing technique are compared to the last. The future scope is the hybrid technique solution to the problem

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A Fuzzy Based Classification – An Experimental Analysis

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j1065.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 4634-4638

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Soft Computing ◽

Disease Diagnosis ◽

Support Vector ◽

Hybrid Technique ◽

Classification Rate ◽

Data Set ◽

Computing Technique ◽

Increase In Accuracy

Soft Computing has become popular in developing systems that encloses human expertise. Imaging technologies and clinical cytology has improved in disease diagnosis. Exact detection is extremely important for proper treatment and cure of disease. Two soft computing technique Neural Network and Support Vector Machine are used for classification of Caridotocography data set. This paper clearly explains the advantages of hybrid technique, when Fuzzy is combined with Neural Network and Support Vector Machine it is clearly noticed that there is an increase in accuracy of classification rate.

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Speaker Recognition System based on Age-related Features using Convolutional and Deep Neural Networks

10.21203/rs.2.23454/v1 ◽

2020 ◽

Author(s):

Karthika Kuppusamy ◽

Chandra Eswaran

Keyword(s):

Neural Network ◽

Speaker Recognition ◽

Speech Rate ◽

Voice Recognition ◽

Gaussian Mixture ◽

Recognition System ◽

Support Vector ◽

Age Related ◽

Novel Approach ◽

Recognition Systems

Abstract With the advent of conversational voice recognition systems growing such as Alexa, SIRI, OK Google, etc., natural language conversational systems including Chatbot and voice recognition systems are in new high and determining the age of a speaker is critical for setting the pertinent context. Age can be inferred from the speech signal by inferring various factors such as physical attributes of voice, linguistic attributes, frequency, speech rate,etc., The proposed research article discusses about extracting the spectral features of speech such as Cepstral Coefficients, Spectral Decrease, Centroid, Flatness, Spectral Entropy, F0DIFF, Jitter and Shimmer as inputs. This would help in classifying speaker age through deep learning techniques. A novel approach is addressed along with the model for implementation using Deep Neural Network and Convolutional Neural Network for classifying the features using three different classifiers which are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and GMM-SVM. The results obtained from the proposed system would outline the performance in speaker age recognition.

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Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽

10.3390/met11040639 ◽

2021 ◽

Vol 11 (4) ◽

pp. 639

Author(s):

Chen Ma ◽

Haifei Dang ◽

Jun Du ◽

Pengfei He ◽

Minbo Jiang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Manufacturing Process ◽

Support Vector ◽

Defect Classification ◽

Droplet Deposition ◽

Network Support ◽

Visual Sensing ◽

Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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