Research Article
GERMIC: Application of Gesture Recognition Model with Interactive Correction to Manual Grading Tasks
@INPROCEEDINGS{10.1007/978-3-319-90740-6_6, author={Kohei Yamamoto and Fumiya Kan and Kazuya Murao and Masahiro Mochizuki and Nobuhiko Nishio}, title={GERMIC: Application of Gesture Recognition Model with Interactive Correction to Manual Grading Tasks}, proceedings={Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018, Osaka, Japan, February 28 -- March 2, 2018, Proceedings}, proceedings_a={MOBICASE}, year={2018}, month={5}, keywords={Handwriting recognition Recognition error correction}, doi={10.1007/978-3-319-90740-6_6} }- Kohei Yamamoto
Fumiya Kan
Kazuya Murao
Masahiro Mochizuki
Nobuhiko Nishio
Year: 2018
GERMIC: Application of Gesture Recognition Model with Interactive Correction to Manual Grading Tasks
MOBICASE
Springer
DOI: 10.1007/978-3-319-90740-6_6
Abstract
Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human’s consecutive motion without reference to retrial or alternation by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user’s grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.
