Greedy Algorithm for Hand Drawn Object Recognition using BPMN

With the fast growing technology of touch screen gadgets, finger stroke gesture interaction has become key buzzword in interactive HCI. The finger touch based gesture recognition can be quite promising task as it varies the human hand skills and behavior. A pen or finger touch gestures are more prominent and convenient than keyboards in all means of exchanging, writing human ideas or thoughts. The naturalness and user ease of a touch-based finger gesture interface offers new opportunities for human-computer interaction. Stroke gesture interfaces are responsible for making gestures analogous to physical effects, keeping gestures simple and distinct, defining stroke gestures systematically, making them self-revealing. Developers are becoming more concerned in creation of finger touch gesture recognizer which plays vital role in ubiquitous computing. Additionally, we hope our approach and findings will provide guidelines to develop a computer-aided system for automatic translating hand-drawn objects to Business Process Management notations. In this paper, the refined greedy algorithm is applied for recognizing Business Process Management notations symbols along with to get recognition accuracy measures. The refined greedy algorithm achieves superior performance than Dynamic Time Warping, the Rubine classifier based recognition, dynamic programming, as well.