Voice Alert System for Traffic Sign Recognition and Nearby Places Finder

This project seeks to develop a web application that combines traffic sign recognition capability with user’s navigation needs. Users will have the ability to upload traffic sign images and capture the live feed from the camera for traffic sign recognition, and an automated system utilizing CNN will be deployed to recognize and encode the sign’s meaning with a speech synthesis. The system is enhanced by the addition of a location service where users can look up the hospitals, restaurants, and gas stations available within a certain distance range from their locations. All the findings are presented to the user as clickable points on a map that shows the best route from the user’s current location to the target place. This project makes use of the Google Maps API to look for locations and draw routes on the map, while Flask captures input data for processing on the server side. This system is implemented in HTML and CSS and JavaScript with the client side being responsive. By integrating location-based technologies using machine learning, the traffic sign detection and real time navigation is fully automated, and it is very useful for various drivers and tourists.

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Publication Details

Journal
International Journal of Innovative Research in Computer Science & Technology
Published
2025-04-01
DOI
https://doi.org/10.55524/ijircst.2025.13.2.5
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Voice Alert System for Traffic Sign Recognition and Nearby Places Finder

G Sai, Sai Jing-bo, S. Venkataramana, J. D. S. Preetham et al.
International Journal of Innovative Research in Computer Science & Technology
Vehicle License Plate Recognition
article

Voice Alert System for Traffic Sign Recognition and Nearby Places Finder

G Sai, Sai Jing-bo, S. Venkataramana, J. D. S. Preetham, K. Jashwanth Sree
article en

Abstract

This project seeks to develop a web application that combines traffic sign recognition capability with user’s navigation needs. Users will have the ability to upload traffic sign images and capture the live feed from the camera for traffic sign recognition, and an automated system utilizing CNN will be deployed to recognize and encode the sign’s meaning with a speech synthesis. The system is enhanced by the addition of a location service where users can look up the hospitals, restaurants, and gas stations available within a certain distance range from their locations. All the findings are presented to the user as clickable points on a map that shows the best route from the user’s current location to the target place. This project makes use of the Google Maps API to look for locations and draw routes on the map, while Flask captures input data for processing on the server side. This system is implemented in HTML and CSS and JavaScript with the client side being responsive. By integrating location-based technologies using machine learning, the traffic sign detection and real time navigation is fully automated, and it is very useful for various drivers and tourists.

International Journal of Innovative Research in Computer Science & TechnologyVol. 13(2)
Hindu College of Pharmacy (IN)
Openalex Percentile: Top 89%
Vehicle License Plate Recognition
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