  Traffic signs are important in communicating information to drivers. Thus,
comprehension of traffic signs is essential for road safety and ignorance may
result in road accidents. Traffic sign detection has been a research spotlight
over the past few decades. Real-time and accurate detections are the
preliminaries of robust traffic sign detection system which is yet to be
achieved. This study presents a voice-assisted real-time traffic sign
recognition system which is capable of assisting drivers. This system functions
under two subsystems. Initially, the detection and recognition of the traffic
signs are carried out using a trained Convolutional Neural Network (CNN). After
recognizing the specific traffic sign, it is narrated to the driver as a voice
message using a text-to-speech engine. An efficient CNN model for a benchmark
dataset is developed for real-time detection and recognition using Deep
Learning techniques. The advantage of this system is that even if the driver
misses a traffic sign, or does not look at the traffic sign, or is unable to
comprehend the sign, the system detects it and narrates it to the driver. A
system of this type is also important in the development of autonomous
vehicles.
