The debilitating nature of medical conditions involving hand tremors can negatively impact use of upper limbs for tasks such as handwriting, potentially resulting in reduced sense of independence, identity and well being. Hand tremor disorders are primarily found in patients with essential/familial tremor and ParkinsonÍs disease (PD), affecting over 11 million people in the US. More than 60 percent of people with PD have issues with abnormally small, cramped writing that tends to taper to smaller size (micrographia). Our project aims to develop a metrically valid, non-invasive and reliable handwriting training method that will improve the handwriting performance (reducing micrographia and/or tremor) for individuals with PD, and other conditions that cause hand tremors. æIn order to assess the effectiveness of such training methods, we developed a series of metrics evaluating features of written text including: text area, quantifiable ink deposit, horizontal and vertical projection profiles of the text, critical points analysis, critical point distance analysis, segment curvature analysis, and optical character recognition analysis. All of these metrics have been implemented on a handwriting sample publicly available from a patient with PD and micrographia which are visually and operationally defined in the poster. æA widely used standard cursive alphabet table has been also tested by some above metrics as control sample. Results obtained with these metrics show, statistically, potential for implementation to quantify and distinguish handwriting performance under symptomatic and asymptomatic conditions.