Recently, deep learning has been increasingly used for robot-based object grasping. Since it is important to accurately recognize the position of objects to be grasped, it is often attempted to grasp the objects through the deep learning scheme which is known to show high object recognition performance. However, large datasets are required for object recognition using deep learning and in order to create a dataset, all data must be collected and needs to be manually annotated. It is a simple task, but takes a lot of time and labor. Therefore, this study reduced the amount of required data and minimized the resources and human effort required for dataset generation through image synthesis method and automatic annotation. Faster R-CNN, the object recognition algorithm, was trained on the generated dataset and recognized the position of the object on the image. The position of an object in an image can be converted to its position in the robot coordinate system using the camera intrinsic parameters which can be obtained by camera calibration. Therefore., the robot can move to the converted position of object on the robot coordinate system to grasp the object. Experiments show that bin picking can be conducted successfully in this way.