# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 1 Introduction  
The road traffic signs must be installed and placed appropriately to ensure that they are clearly visibility to the road users. Though the traffic sign has standard physical structure and appearance, various natural issues and human errors cause the variation in color, shape, or both. Moreover, most of the time a valuable sign is ignored by the driver and puts them in a potentially dangerous situation because of the lack of attention and unclear visibility [1]. Additionally, in many places, the visibility of road traffic signs may get obscured; consequently drivers may unintentionally ignore them. Though road users can recognize and classify distinct road signs without any mistake or delay, robust and fully automated detection and recognition of the road sign remains a challenge for autonomous vehicles. Currently, many researchers and vehicle manufacturing companies are designing autonomous vehicles [2]. These vehicles can operate without the intervention of a human driver. Hence, such autonomous vehicles need the robust traffic sign detection and recognition system to be in place for them to operate on the road safely. Recognition of traffic signs has been receiving more attention in recent years due to the advanced integrated system for smart vehicles. Due to upgrading to a higher living standard, the communicationand transportation system has improved, and the use of vehicles has also increased. With the increasing number of vehicles, the rate of traffic accidents has also increased. At present, road traffic accidents have become one of the most frequent causes of death worldwide [3]. By 2030, road accidents will be the fifth most common cause of death around the world [4], including in Malaysia [5]. Numerous research organizations are currently working to reduce road-related incidents by integrating transportation systems with artificial intelligence as an advanced driver assistance system. An advanced driver assistance system automatically detects traffic signs by using a camera mounted on the dashboard of a vehicle; this functions as a road sign recognition system. This system helps the driver to be aware of the road and traffic signs, rules of driving along the road and notifies the driver of the said signs which ultimately help reduce the possibility of having an accident. An intelligent traffic sign classification system is a vital function of an intelligent transport system [6; 7]. The automatic traffic sign identification involves two main stages namely, detection, and recognition. The detection stage is performed to identify the region of interest (ROI) generally by using the color segmentation, followed by some form of shape identification. Detected traffic sign candidates are then either recognized or rejected through the recognition stage. The recognition stage is performed with some machine learning techniques such as the artificial neural network (ANN) [8; 9; 10; 11; 12; 13; 14], support vector machine (SVM) [15; 16; 17; 18; 19; 20; 21; 22] or the template matching [23]. Recently, many researchers have proposed various traffic sign detection and recognition systems and they achieved satisfactory results [24]. Though existing systems can achieve fast processing speed and classification accuracy, the existing systems can only detect and recognize well-maintained and standard traffic signs. Moreover, these existing systems may not work properly for un-maintained and non-standard traffic signs. In many developing countries, to maintain the traffic sign may not be the top priority of respective authorities and these signs may get affected by environmental factors such as rain, illumination, pollution, damage due to accident or human error, etc. For instance, consider Figure 1a where the traffic sign has been faded due to continues sunlight. Furthermore, many countries have different colors and shapes for traffic signs identification because of their climate and other environmental factors.