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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Akash Kumar
Birla Institute of Technology Mesra Patna Campus
Computer Science & IT Engineering

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Suggestive GAN for supporting Dysgraphic drawing skills Smita Pallavi; Akash Kumar; Abhinav Ankur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1276.467 KB) | DOI: 10.11591/ijai.v8.i2.pp132-143

Abstract

The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the actionmotion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.
Co-Authors Abhinav Ankur Smita Pallavi