# Robotic-Based Touch Panel Test System Using Pattern Recognition Methods
## 1 Introduction  
In the past decade, intelligent control has brought a huge change to our daily lives. Image processing and robot arms play important roles in many intelligent control designs. There are a lot of applications of image processing and robot arms, such as medical surgery, automatic manufacturing, home care, food processing, etc. According to target positions, robot movement needs to be designed for high precision and stability in the control process. As robotic technologies improve in a variety of ways, many difficult or time-consuming tasks can be performed by robots with high precision and recognition capability [1; 2; 3; 4]. Nowadays, global industry is moving to Industry 4.0, which holds the promise of increased flexibility in manufacturing, better quality, and improved productivity [5]. This study is mainly focused on improving the efficiency of smartphone testing procedures so that the proposed system can replace the labor force in routine smartphone tests. Intelligent robots have become a key part in many applications [6; 7; 8; 9; 10]; they can provide health care, home service, entertainment, industrial automation, etc. Intelligent robots are used in many research fields, such as for path planning, obstacle avoidance, simultaneous localization and mapping, visual servoing control and image processing [11; 12; 13]. Expert knowledge has been integrated into the robot system design that makes interactions between human and robot harmonious. The objective of this study is to replace manual smartphone testing with automatic robot arm adapted to the Chinese character environment. The proposed control schemecan automatically test a smartphone by the use of a robotic system. With support vector machine (SVM) [14], a histogram of oriented gradients (HOG) [15], neural networks, and fuzzy theory, complex computation [16] can be reduced in the control process. [PERSON], [PERSON] et al. [17] improved SVMs and proposed a method to generate nonlinear classifiers using the kernel trick to the maximum margin hyperplane. With HOG describing local object appearance and shape within an image by the distribution of intensity gradients or edge directions, the SVM was able to classify data more successfully. The method utilized for coordinate conversion so that the robot arm can be driven to target positions is the Denavit-Hartenberg parameters (D-H) model [18]. In our study, the target positions are on the smartphone, which are the desired buttons to be pressed. Image processing and pattern recognition techniques are needed to locate the target button position. The webcam captures an image using Red-Green-Blue (RGB) color, which is easily affected by lightness. Hue, saturation and lightness (HSL) color space is usually applied to reduce this effect. The histogram of oriented gradient (HOG) can recognize features of the picture. With HOG describing local object appearance and shape within an image by the distribution of intensity gradients or edge directions, the SVM will be able to classify data more successfully. The SVM-HOG can recognize symbol characters on the screen of the smartphone and ignore the effect of lightness and camera angle. Thus, the HSL transformation process can be omitted, which reduces the calculation burden. In image processing, we do not have to convert the image to HSL color space or another form of binary image. This is an advantage of SVM-HOG in system processes. In optical character recognition, the accuracy of Asian characters documents is usually lower than that for English documents [19; 20; 21]. While using optical character recognition (OCR) [22; 23] on Chinese characters and Chinese Mandarin phonetic symbol recognition, it is hard to use OCR to compare symbol characters and pictures because the system is based on an English environment. Besides, the performance of OCR is low on symbol recognition. This study replaces OCR character recognition by picture comparison similar to car detection and traffic sign detection.