• Shaik Mohammad Shareef
• Kanusu Jyothi
• Bammidi Hari Krishna
• Kotapati Venu Madhav
• Dr.V.Balaji

Traffic signs are vital for showing information to drivers, and they are fairly important for road safety. Failure to detect or understand these signs could pose risks, underscoring the robust detection systems importance. This study describes a voice-led traffic sign recognition system that operates in real-time to assist drivers. A pre-trained Convolutional Neural Network (CNN) on the back end handles detection and recognition, while a text-to-speech engine for the driver provides the narration. We propose an efficient traffic sign detection and classification technique that achieves state-of-the-art performance on the German traffic sign recognition benchmark GTSRB while needing minimal processing resources and working in real time, using a model trained on a large dataset.With this two-pronged approach, you should be protected even in the event that a motorist disregards a sign because the system will detect and transmit it. Through the use of Convolutional Neural Networks (CNNs) to narrate recognized signs to drivers in text format, this system not only advances the development of autonomous vehicles and intelligent transportation systems, but it also benefits drivers in general. The suggested algorithm's usefulness is demonstrated by the experimental findings, which also offer support for its potential usage in advanced driver assistance systems, traffic management, and autonomous driving.

"TRAFFIC SIGN RECOGNITION USING CNN WITH VOICE ASSISTANCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.g110-g118, June-2024, Available :http://www.jetir.org/papers/JETIR2406619.pdf

"TRAFFIC SIGN RECOGNITION USING CNN WITH VOICE ASSISTANCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppg110-g118, June-2024, Available at : http://www.jetir.org/papers/JETIR2406619.pdf