# Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks
## 4 Experimental Results
Then, its coordinates are transferred to the robot arm for grabbing. Since the chemsmen are randomly placed on the chessboard, the order of grabbing begins from the chemsmen placed on both sides of the chessboard until all chemsmen are placed, as shown in Figure 18. If there are back-side chemsmen, the system can identify the situation and notify the robotic arm to turn over those first, and then perform image recognition, as shown in Figure 19. Figure 20 depicts the complete process of chess placement, beginning with the interface, locating the first chemsman at the side of the chessboard, turning over the back-side chemsman, finally finishing the placement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Real Coordinate** & **Coordinate before Correction** & **Error** & **Coordinate after Correction** & **Error** \\ \hline (64,483) & (50,488) & 14.86 & (64.9,486) & 3.13 \\ (188,578) & (175,581) & 13.34 & (186.2,578) & 1.8 \\ (186,379) & (174,381) & 12.16 & (185.2,381.3) & 2.44 \\ (314,691) & (303,698) & 13.04 & (311.5,692.5) & 2.92 \\ (322,278) & (313,274) & 9.85 & (321.3,277.5) & 0.86 \\ (440,780) & (436,791) & 11.70 & (441.6,783.5) & 3.85 \\ (452,174) & (448,170) & 5.66 & (454.4,174.8) & 2.53 \\ (822,171) & (826,168) & 5 & (823.3,173.8) & 3.09 \\ (826,786) & (831,794) & 9.43 & (828.2,786.4) & 2.24 \\ (952,686) & (958,692) & 8.49 & (952.5,686.6) & 0.78 \\ (965,290) & (969,287) & 5 & (963.3,290.3) & 1.73 \\ (1074,387) & (1085,385) & 11.18 & (1076.8,386.2) & 2.91 \\ (1082,590) & (1090,593) & 8.54 & (1081.7,589.7) & 0.42 \\ (1220,488) & (1230,493) & 11.18 & (1218.7,491.9) & 4.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coordinates before and after correction and errors between them and real ones. Figure 15: Locations of chemsmen of actual, uncorrected, and corrected. Figure 16: Original image and its binarized one. (**a**) Original captured image, (**b**) binarized image. Figure 17: Cut-out images of each chessman. (**a**) The red colored chessman \(\sharp\), (**b**) the black colored chessman \(\sharp\), (**c**) the back-side chessman. Figure 18: _Cont_. Figure 19: _Cont._  
Figure 18: The process of robotic arm grabbing. (**a**) The robotic arm picking a random black chessman, (**b**) the robotic arm placing the chessman to its correct location, (**c**) the robotic arm picking another random black chessman, (**d**) the robotic arm placing the chessman to its correct location, (**e**) the robotic arm picking the first random red chessman, (**f**) the robotic arm placing the chessman to its correct location. Figure 19: Turning over the back-side chessman and recognizing. **(a)** First, turning over a back-side chessman, **(b)** turning over the back-side chessman, **(c)** next, putting the chessman back, **(d)** then, putting it in front of the camera to be recognized, **(e)** lastly, the chessman is captured by the camera, **(f)** the chessman is recognized by the camera and the cut-out image is captured. Figure 20: _Cont._  
In summary, correct recognition will have the correct picking and placing of the chessmen. The failure cases come from three points, the first: the chessmen \(\circled{H}\) and \(\circled{H}\) have the same radical of Chinese characters; the second: there may be an erroneous recognition of chessman \(\circled{H}\) with the same radical of \(\circled{H}\) and \(\circled{H}\); and the third: the strokes of these three chessmen are more likely to affect the recognition result. As a result, another auxiliary way may be included to eliminate these cases.