Traffic informatory signs, which is a category of traffic signs and text-based signs, is very important to both drivers and intelligent transport systems. Previous studies have usually sought to extract text lines in signs to apply to optical character recognition (OCR) system, but they do not work well in real-world conditions with severe disturbance. In this paper, we report on our study of place name text detection and recognition on traffic informatory signs using convolutional neural networks (CNNs) and transform traditional text detection and recognition into word-level multi-class robust image classification. In our study, each place name corresponds to one class. Because the number of word classes is large and collecting real images for training dataset is difficult, we generate several synthetic datasets mainly by means of two methods and use them to train the CNN respectively. One method generates with standard templates of the signs, while the other method renders text in natural images which have no relationship with the signs. Our experimental results show that our method can achieve high levels of accuracy when reading traffic informatory signs in real-world conditions. Accuracy of 0.891 and 0.981 are achieved in the former and latter method of generating dataset, which verify that proposed methods are effective in our task. We also analyze the dependence of color channels in text detection during our task, which help generate the more efficacious synthetic dataset.