A feature based comparison of pen and swipe based signature characteristics

Evolutionary Metric-Learning-Based Recognition Algorithm for Online Isolated Persian/Arabic Characters, Reconstructed Using Inertial Pen Signals

The development of sensors with the microelectromechanical systems technology expedites the emergence of new tools for human-computer interaction, such as inertial pens. These pens, which are used as writing tools, do not depend on a specific embedded hardware, and thus, they are inexpensive. Most of the available inertial pen character recognition approaches use the low-level features of inertial signals. This paper introduces a Persian/Arabic handwriting character recognition system for inertial-sensor-equipped pens. First, the motion trajectory of the inertial pen is reconstructed to estimate the position signals by using the theory of inertial navigation systems. The position signals are then used to extract high-level geometrical features. A new metric learning technique is then adopted to enhance the accuracy of character classification. To this end, a characteristic function is calculated for each character using a genetic programming algorithm. These functions form a metric kernel classifying all the characters. The experimental results show that the performance of the proposed method is superior to that of one of the state-of-the-art works in terms of recognizing Persian/Arabic handwriting characters.

Graphonomics and its contribution to the field of motor behavior: A position statement

The term graphonomics was conceived in the early 1980s; it defined a multidisciplinary emerging field focused on handwriting and drawing movements. Researchers in the field of graphonomics have made important contribution to the field of motor behavior by developing models aimed to conceptualize the production of fine motor movements using graphical tools. Although skeptics have argued that recent technological advancements would reduce the impact of graphonomic research, a shift of focus within in the field of graphonomics into fine motor tasks in general proves the resilience of the field. Moreover, it has been suggested that the use of fine motor movements due to technological advances has increased in importance in everyday life. It is concluded that the International Graphonomics Society can have a leading role in fostering collaborative multidisciplinary efforts and can help with the dissemination of findings contributing to the field of human movement sciences to a larger public.