Document Type
Conference Proceeding
Publication Date
6-1-2025
Abstract
Monitoring children’s handwriting, such as avoiding writing assignments, displaying uneven letter formation, or showing slow writing speed, can help identify developmental and academic issues early. Poor handwriting affects up to 34% of children, leading to academic and self-esteem challenges. Handwriting assessments, typically conducted by teachers, are often delayed due to workload and could be subjective and inconsistent. This paper explores the potential of Optical Character Recognition (OCR) technology to augment and ease handwriting assessments. Based on an evaluation of 10 OCR algorithms using 33 handwriting samples assessed by two experts, the research indicates that Pen to Print and Google are the most accurate for character recognition. However, Handwriting to Text Converter and Smart Text Recognizer align more closely with expert evaluations. These findings suggest that the latter algorithms are better suited for supporting handwriting assessments. Personalized, targeted letter practice guided by OCR-based substitution analysis could enhance handwriting outcomes and tailored feedback.
Recommended Citation
Cibrian, F.L., Anderson, K., Chen, Y.‘., Min, L., Escobedo, L. (2025). Optical Character Recognition for Early Handwriting Legibility Assessment. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2025. Lecture Notes in Computer Science, vol 15767. Springer, Cham. https://doi.org/10.1007/978-3-031-93838-2_11
Copyright
The authors
Included in
Educational Assessment, Evaluation, and Research Commons, Other Computer Sciences Commons, Other Electrical and Computer Engineering Commons
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Human-Computer Interaction. HCII 2025. Lecture Notes in Computer Science, volume 15767, in 2025 following peer review. The final publication may differ and is available at Springer via https://doi.org/10.1007/978-3-031-93838-2_11.