Online language and literacy assessments have become prevalent in research and practice across settings. However, a notable exception is the assessment of handwriting and spelling, which has traditionally been conducted in person with paper and pencil. In light of this, we developed an automated, browser-based handwriting test application (Online Assessment of Handwriting and Spelling: OAHaS) for Japanese Kanji (Study 1) and examined its psychometric properties (Study 2). The automated scoring function using convolutional neural network (CNN) models achieved high recall (98.7%) and specificity (84.4%), as well as high agreement with manual scoring (95.4%). Additionally, behavioral validation with data from primary school children (N = 261, 49.0% female, age range = 6–12 years) indicated the high reliability and validity of our online test application, with a strong correlation between children’s scores on the online and paper-based tests (r = .86). Moreover, our analysis indicated the potential utility of writing fluency measures (latency and duration) that are automatically recorded by OAHaS. Taken together, our browser-based application demonstrated the feasibility and viability of remote and automated assessment of handwriting skills, providing a streamlined approach to research and practice on handwriting. The source code of the application and supporting materials are available on Open Science Framework (https://osf.io/gver2/).
