Abstract: Automated tasks have simplified almost everything we perform in today's environment. Due to a desire to focus only on driving, drivers regularly ignore signs placed on the side of the road, which can be harmful to themselves and others. To address this issue, the motorist should be informed in a method that does not require them to divert their concentration. Traffic Sign Detection and Recognition (TSDR) is critical in this case since it alerts the motorist of approaching signals. Not only are roads safer because of this, but motorists also feel more at ease when driving unfamiliar or difficult routes. Another typical issue is inability to read the sign. Driver assistance systems (ADAS) will make it easier for motorists to read traffic signs with the help of this software. We provide a traffic sign detection and recognition system that employs image processing for sign detection and an ensemble of Convolutional Neural Networks (CNNs) for sign recognition. Because of its high recognition rate, CNNs may be used in a wide range of computer vision applications. TensorFlow is used in CNNTSR (Traffic Sign Recognition), a key component of current driving assistance systems that improves driver safety and comfort. TensorFlow is used to implement CNNTSR (Traffic Sign Recognition). This article examines a technology that assists drivers in recognizing traffic signs and avoiding road accidents. Two things determine the accuracy of TSR: the feature extractor and the classifier. Although there are a variety of approaches, most recent algorithms use CNN (Convolutional Neural Network) to do both feature extraction and classification tasks. Using TensorFlow and CNN, we create traffic sign recognition. The CNN will be trained using a dataset of 43 distinct types of traffic signs. The accuracy of the findings will be 95 percent. Keywords: Driver, Tensor flow, Data Sheet, Alert, CNNTSR, ADAS.