A software testbed for digital handwriting analysis and training

In many skilled labor jobs, workers develop extremely valuable specialized knowledge about the jobs they perform. Large manufacturing companies, such as Rolls-Royce Corporation, see great value in developing software systems that can monitor skilled workers and quantify the characteristics associated with productive performance. The quantification of the characteristics associated with the most highly skilled workers could later be built into training or automation systems for skilled tasks. The goal of this project was to create a software system that is able to capture the expert knowledge of a skilled user on a particular task. The design effort explored the use of individual handwriting styles. The project team created a system that recognized and captured the subtle nuances of a person's handwriting. The algorithm created used an individual's writing sample to identify characteristics that made that individual's writing style unique and recognized one person's handwriting over another person's handwriting. The information captured could later be used in experiments in which an individual can try to mimic the handwriting style of an "expert" and gain feedbacks for improvement. The handwriting recognition system showed a proof-of-concept for knowledge management software in the absence of a highly technical process.