## 1 Introduction  
People with visual impairment (PVI) have limitations in the function of their visual system. These limitations prevent them from seeing and performing daily activities such as navigation or shopping [1; 2; 3; 4; 5]. Therefore, it is important to develop solutions to help PVI improve their mobility, protect them from injury, and encourage them to interact socially [6]. PVI also face challenges when navigating in shops, and to access and identify products [7]. While outdoor navigation is already solved by the use of a global positioning system, indoor navigation is still problematic [8; 9; 10; 11]. As a result, multiple researches have been carried out to help PVI in indoor navigation and identifying products in shops or other objects using Wi-Fi, Bluetooth, RFID, NFC, and tags. These solutions differ in the types of the signals used, the positioning method, and the accuracy [12; 13; 14; 15; 16; 17; 18; 19]. These solutions fall into three categories: Tag Based Systems: Such as radio-frequency identification (RFID) and near field communication (NFC), which use wireless components to transfer data from a tag attached to an object for the purpose of automatic identification and tracking [20; 21]. Computer Vision Based Systems: These systems install or attach unique visual tags such as quick response (QR) codesor augmented reality (AR) markers to help in the navigation and recognition process [22; 23; 24; 25]. Other systems do not require tags to be installed as they use computer vision (CV) techniques to analyze images and extract some features from them which help in navigating and identifying objects [26; 27; 28; 29; 30; 31; 32; 33]. Hybrid Systems: These systems take the strengths of two or more systems and combine them into a new system to deliver better accuracy and performance [34; 35; 36]. In this research, the authors have focused on CV techniques. CV non-tag-based systems are cost-effective, as they need little or no infrastructure and most of them can be easily installed on smartphones. However, they have several limitations. Their performance may be unpredictable in real-world environments, as image resolution may be affected by some factors including motion blur and changes in certain conditions, such as illumination, orientation, and scale. These systems also use extensive computational power which is not suitable for real-time usage. It is also difficult for PVI to capture photos in good quality [26; 37]. CV tag-based systems rely on detectable visual landmarks such as QR codes, barcodes, and AR markers, which improve precision, robustness, and speed [5; 37]. However, CV tag-based techniques require a prior installation of tags in the correct place and building a map to store these locations. Moreover, if many tagged items are installed in a small area, PVI may be confused by receiving information about all of them at the same time. Furthermore, tags not related to the system may confuse PVI. These tags are susceptible to damage caused by movement through the supply chain or by the weather. Moreover, it is difficult for a smartphone camera to detect CV tags if the PVI are moving fast, and the recognition rate decreases as the distance between the reader and the tags increases [38]. Based on our evaluation of the available technologies, the authors have concentrated on CV tag-based techniques. Our goal is to build an indoor navigation system for PVI using CV tag-based techniques. A wide range of tags may be used, but QR codes and square markers are the most popular ones as they provide four correspondence points which are enough to perform camera position estimation. However, captured images in many real-life applications may have some problems such as blur due to motion, distortion of marker, and marker occlusion which makes identification difficult. The authors proposed a navigation system to improve marker identification in challenging conditions using convolutional neural network (CNN). By using CNN, the identification problem is converted to a typical classification problem.