Learnart : Drawing Environment using Convolutional Neural Networks
Duraimurugan. N1, Manoj Kumar. B2, Malini. C3, Kowsalya. R4

1Duraimurugan. N, Department of Human Resources, Chennai (Tamil Nadu), India.
2Manojkumar. B, Student, St. Josephs College of Commerce, Museum Road, Banglore (Karnataka), India.
3Malini. C, Director – Quality, Bengaluru (Karnataka), India.
4Kowsalya. R, Department of Software Engineer, Tiruchirappalli (Tamil Nadu), India.
Manuscript received on 20 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 770-772 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11420782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1142.0782S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Simulating human consciousness and emotions is still the realm of science fiction. The future of neural networks will not exist in tasks to simulate realization. Nowadays, further learning is based on training the machine to recognize the target that might be image or words using datasets. The project is based on convolutional neural networks and weak artificial intelligence. It uses Back Propagation algorithm. It acts as a smart drawing platform for children. Predefined datasets will be framed when it is developed so that users cannot alter it. Users will be the one who draw and checks its efficiency. Suggestion will be displayed if it is right or wrong. The drawings will be shapes and real time objects. The platform gets trained by all these datasets and recognizes the object. The drawing is stored as pixels pattern and checks with the previous data and finds how much percentage does the current drawing is matching with the previous drawings. By the help of this application, children can develop basic single object drawing.
Keywords: Artificial Intelligence, Deep Learning, Convolutional Neural Networks, Back Propagation.
Scope of the Article: Neural Information Processing