# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
## 4 Discussion
CV tag based systems offer several advantages: they only need to identify tags to get product details, so they need low computational power and small storage space. Many of these approaches do not need tags to be explicitly placed, as products already have unique visual tags, such as barcodes and QR codes. Such tags can be generated and printed at a very low cost compared to non-visual tags, such as RFID, and can be easily modified. CV tag based systems are ideal for tasks that require differentiating among groups of objects. They are vital for PVI when the contents of the items are different, such as a tube of glue versus a tube of eye drops, as they have the same shape and it may be dangerous if they choose the wrong one ([PERSON], 2018). However, tag based CV techniques require a prior selection of items and the correct placement of tags on those items. Moreover, if there are many tagged items in a small area, PVI would be confused by receiving information about them all at the same time. Visual tags also must be in line-of-sight of the camera, otherwise, they will not be detected. Furthermore, visual tags cannot be placed inside items, as the appearance of these tags are important for PVI. These tags can also be damaged during movement through the supply chain or by weather. Also, it is difficult for a smartphone camera to detect CV tags if the PVI is moving fast, and the recognition rate decreases as the distance between the reader and the tags increases ([PERSON], 2018). CV non-tag based systems have several advantages. These 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 unreliable in real-world environments because of factors like motion blur and image resolution, as well as changes in conditions, such as illumination, orientation, and scale. These systems use extensive computational power, and PVI need to take many photos. However, taking good quality photos is difficult for PVI. Finally, feedback latency must be reduced to make these systems more effective. Finally, researchers have created hybrid systems by taking the strengths of two or more systems and combining them. Numerous attempts have been made in this area to balance the trade-offs of the combined technologies. As a result, there is a significant improvement in accuracy, robustness, performance, and usability. However, the major drawback of these systems is that they use significant infrastructure due to the combination of technologies, which results in increased complexity and cost. Figure 3 shows a complete scenario of the shopping process, from preparation of the shopping list to completion of the shopping task. To summarize, the answers to Q1, Q2, and Q3 are:  
**A1:** This study presented the main categories of MAT to help PVI shop. We divided the shopping task into two parts: 1) Preparing a list of what to buy, and 2) navigation inside a store and identification of items on the shelves. Preparing a list for shopping solutions used techniques such as CV and STT to obtain list details from PVI and store them in a database. During shopping, audio feedback is given to PVI about shopping items using TTS. The navigation and identification of products use RFID and NFC for tag based systems, visual tags like QR codes, barcodes and AR markers for CV tag based systems, or information about the physical features of items for CV approaches  
**A2:** Table 1 shows the strengths and weaknesses of the latest MAT shopping systems for PVI. Figure 3: The scenario of the shopping solutions for PVI. Based on the categories and solutions discussed in Section 2, we selected some criteria and compared them in Table 2. The first criterion is the cost of applying the technology to any solution. It is shown that CV tag techniques can be used without any cost except for printing the QR codes or AR markers and putting them in the correct place.