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Journal of IoT in Social, Mobile, Analytics, and Cloud

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

Design of Deep Learning Algorithm for IoT Application by Image based Recognition
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Analysis of Serverless Computing Techniques in Cloud Software Framework
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Health Record Management System – A Web-based Application
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A Novel Signal Processing Based Driver Drowsiness Detection System
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IoT Based Monitoring and Control System using Sensors
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Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
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Review of Internet of Wearable Things and Healthcare based Computational Devices
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Stock Index Prediction with Financial News Sentiments and Technical Indicators
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Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Volume-3 | Issue-3

Suspicious Human Activity Detection System
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ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3

EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2

Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4

IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3

IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1

Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3

Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4

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Volume - 3 | Issue - 3 | september 2021

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Pages: 191-205
Published
14 August, 2021
Abstract

Automatically identifying traffic signs is a challenging and time-consuming process. As the academic community pays more attention to traditional algorithms for vision-based detection, tracking, and classification, three main criteria drive the investigation, they are detection, tracking, and classification. It is capable of performing detection and identification operations to minimize traffic accidents and move towards autonomous cars. A novel method proposed in this paper is based on moment invariants and neural networks for performing detection and recognition with classification, and it also includes automatic detection and identification of traffic signs and traffic board text that uses colour segmentation. Aside from the proposed structure, it is also required to identify the potential graphic road marking with text. This research article contains two algorithms, which are used to accurately classify the board text. The detection through image segmentation and recognition can be done by using the CNN algorithm. Finally, the classification is performed by the SVM framework. Therefore, the proposed framework will be very accurate and reliable with high efficiency, which has been proven in many big dataset applications. The proposed algorithm is tested with various datasets and provided good identification rate compared to the traditional algorithm.

Keywords

Machine learning CNN SVM

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