# Multimedia Vision for the Visually Impaired through 2D Multiarray Braille Display
## 3 Proposed eBook Reader Application for 2D Braille Display
### Components and the Design of 2D Braille Display for the VI
#### 3.4.3 Braille Image Conversion on Braille Display for the VI  
There have been collaborated studies on braille figures, which presented novel methodologies for translating texts and graphics to braille. Figure 12 explains the translated braille image in a multiarray braille display based on such studies [19; 20; 21]. Generally, braille graphic experts consider three factors while constructing braille graphics: image complexity classification, a tactile graphic translation of low- or high-complexity images, and image segmentation. For image complexity classification, simple graphic images are defined by graphs and tables. On the other hand, pictures and paintings are distinguished as intricate images for processing. A comparison of the gray level histograms for simple and complex images shows a clear distinction between the two. Generally, simple images such as charts and graphs have drastic changes in their gray level pixels unlike complex images that have slight changes between their gray level pixels. Therefore, high-complexity images cannot be expressed in braille perfectly; and extracting and displaying a central object on the braille display is an efficient method. Especially, the main reason for extracting only the central object is explained in Table 4. According to [PERSON] et al. [19], blind people find it difficult to recognize braille images without central objects. Because VI users usually begin from the top-left side of the braille display and move towards the bottom-right side, it is difficult to understand the high-complexity images in the braille display at a glance, although they are translated very well. In addition, they could not distinguish other image features such as color and texture through touch. Therefore, the edges of the central object,which can be conversed by braille, were extracted and presented on the braille device. The central object calculates the color differences in one image and labels them for extraction. Additionally, it is assumed that the central object is located at the center of the image, and is detected through the following steps: (i) simplify color values by quantization; and (ii) obtain the color similarities and extract color values divided by each color in the image. Generally, based on the photography and characteristics of the triangulation, the largest area within these labeled areas is designated as a central label. However, the central object may not be able to capture this properly because it has a small area in the central label area. In this context, especially because of the large area of the landscape, a horizontal or vertical aspect of the object can lead to errors that cannot be captured correctly by the central object. This is done by calculating the variance by the size and average of the horizontal or vertical pixels of the central object located in the central label area and matching with the decision-making values. In this study, the decision-making values were directly extracted by five tactile graphic experts. The tested figures are in textbooks for high school students. Subsequently, the edges of the central object area were converted to binary data and the borders of the binary figure were extracted. In this binary image, there are some irregular sharp lines in the contours, which are unnecessary and disturb the lines of the edges; these should be simplified because of the recognition problem outlined in Table 4. Expansion and segmentation were conducted on the binar