Version 1
: Received: 6 May 2018 / Approved: 7 May 2018 / Online: 7 May 2018 (10:11:23 CEST)
How to cite:
Yamamoto, K.; Kan, F.; Murao, K.; Mochizuki, M.; Nishio, N. Manual Grading Task Support System with Interactive Correction Mechanism. Preprints2018, 2018050112. https://doi.org/10.20944/preprints201805.0112.v1
Yamamoto, K.; Kan, F.; Murao, K.; Mochizuki, M.; Nishio, N. Manual Grading Task Support System with Interactive Correction Mechanism. Preprints 2018, 2018050112. https://doi.org/10.20944/preprints201805.0112.v1
Yamamoto, K.; Kan, F.; Murao, K.; Mochizuki, M.; Nishio, N. Manual Grading Task Support System with Interactive Correction Mechanism. Preprints2018, 2018050112. https://doi.org/10.20944/preprints201805.0112.v1
APA Style
Yamamoto, K., Kan, F., Murao, K., Mochizuki, M., & Nishio, N. (2018). Manual Grading Task Support System with Interactive Correction Mechanism. Preprints. https://doi.org/10.20944/preprints201805.0112.v1
Chicago/Turabian Style
Yamamoto, K., Masahiro Mochizuki and Nobuhiko Nishio. 2018 "Manual Grading Task Support System with Interactive Correction Mechanism" Preprints. https://doi.org/10.20944/preprints201805.0112.v1
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.
Keywords
handwriting recognition; recognition error correction; human computer interaction
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
