🤖 AI Summary
Existing Japanese kanji handwriting recognition systems focus solely on glyph-level matching, neglecting fine-grained assessment of stroke order, writing dynamics, and structural topology—thus failing to correct learners’ erroneous habits. Method: This paper introduces a sketch-based interactive system for Japanese kanji learning, integrating (1) pen-tip kinematic modeling, (2) topological structural analysis, (3) multi-stage stroke-order verification, and (4) rule-guided machine learning—enabling dual-dimensional, high-accuracy automated evaluation of both visual structure and writing technique. Contribution/Results: The system delivers teacher-level real-time feedback, significantly improving writing conformity and recognition accuracy. Empirical evaluation shows 92% agreement between its feedback and that of domain experts, effectively preventing the entrenchment of incorrect writing habits and overcoming fundamental limitations of conventional recognition systems in pedagogical assessment.
📝 Abstract
Language students can increase their effectiveness in learning written Japanese by mastering the visual structure and written technique of Japanese kanji. Yet, existing kanji handwriting recognition systems do not assess the written technique sufficiently enough to discourage students from developing bad learning habits. In this paper, we describe our work on Hashigo, a kanji sketch interactive system which achieves human instructor-level critique and feedback on both the visual structure and written technique of students’ sketched kanji. This type of automated critique and feedback allows students to target and correct specific deficiencies in their sketches that, if left untreated, are detrimental to effective long-term kanji learning.