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.
Hammond, Tracy Anne (Texas A&M University) | Logsdon, Drew (Texas A&M University) | Paulson, Brandon (Texas A&M University) | Johnston, Joshua (Texas A&M University) | Peschel, Joshua (Texas A&M University) | Wolin, Aaron (Texas A&M University) | Taele, Paul (Texas A&M University)
Military course-of-action (COA) diagrams are used to depict battle scenarios and include thousands of unique symbols, complete with additional textual and designator modifiers. We have created a real-time sketch recognition interface that recognizes 485 freely-drawn military course-of-action sym- bols. When the variations (not allowable by other systems) are factored in, our system is several orders of magnitude larger than the next biggest system. On 5,900 hand-drawn symbols, the system achieves an accuracy of 90% when con- sidering the top 3 interpretations and requiring every aspect of the shape (variations, text, symbol, location, orientation) to be correct.
We present a new corner finding algorithm based on merging like stroke segmentations together in order to eliminate false positive corners. We compare our system to two benchmark corner finders with substantial improvements in both polyline and complex fits. Sketch recognition is an emerging field that utilizes penbased interaction with computers. Handwriting recognition software in the modern operating systems allows users to write naturally, and applications have been created to recognize sketches in domains such as UML diagrams (Hammond & Davis 2002) and family trees (Alvarado & Davis 2004). In an attempt to perform free-sketch recognition, which allows users to draw as they would naturally without training or being trained by the system, certain geometric sketch recognition systems require a shape to be defined by a set of primitives (Hammond & Davis 2007).
Learning music theory not only has practical benefits for musicians to write, perform, understand, and express music better, but also for both non-musicians to improve critical thinking, math analytical skills, and music appreciation. However, current external tools applicable for learning music theory through writing when human instruction is unavailable are either limited in feedback, lacking a written modality, or assuming already strong familiarity of music theory concepts. In this paper, we describe Maestoso, an educational tool for novice learners to learn music theory through sketching practice of quizzed music structures. Maestoso first automatically recognizes students' sketched input of quizzed concepts, then relies on existing sketch and gesture recognition techniques to automatically recognize the input, and finally generates instructor-emulated feedback. From our evaluations, we demonstrate that Maestoso performs reasonably well on recognizing music structure elements and that novice students can comfortably grasp introductory music theory in a single session.