A Simple Convolutional Neural Network for Doodle Classification

Nithin Deepkumar; Shilpa Ankalaki

doi:10.1109/NKCon56289.2022.10126970

A Simple Convolutional Neural Network for Doodle Classification

Nithin Deepkumar
, Shilpa Ankalaki

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Visuals have been an integral part of our lives. The use of hand-drawn doodles is widespread and persistent, enabling people to retain information better and freely communicate thoughts and information. Doodle classification / hand-drawn diagram classification has significant application to communicate with persons who are deaf or hard of hearing that trains them to identify their urgent needs. The objective of this work is to develop a convolutional neural network (CNN) to classify the hand drawn images. This work utilized the Quickdraw dataset to train and evaluate the model. The performance of the customized model is compared with various state-of-the-art deep learning algorithms like MobileNet and DenseNet architectures and proved that proposed model comparatively gives better results on Quickdraw dataset.

Original language	English
Title of host publication	2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665453424
DOIs	https://doi.org/10.1109/NKCon56289.2022.10126970
Publication status	Published - 2022
Event	2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022 - Vijaypur, India Duration: 20-11-2022 → 21-11-2022

Publication series

Name	2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

Conference

Conference	2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022
Country/Territory	India
City	Vijaypur
Period	20-11-22 → 21-11-22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems
Information Systems and Management
Renewable Energy, Sustainability and the Environment
Safety, Risk, Reliability and Quality

Access to Document

10.1109/NKCon56289.2022.10126970

Cite this

Deepkumar, N., & Ankalaki, S. (2022). A Simple Convolutional Neural Network for Doodle Classification. In 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022 (2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NKCon56289.2022.10126970

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title = "A Simple Convolutional Neural Network for Doodle Classification",

abstract = "Visuals have been an integral part of our lives. The use of hand-drawn doodles is widespread and persistent, enabling people to retain information better and freely communicate thoughts and information. Doodle classification / hand-drawn diagram classification has significant application to communicate with persons who are deaf or hard of hearing that trains them to identify their urgent needs. The objective of this work is to develop a convolutional neural network (CNN) to classify the hand drawn images. This work utilized the Quickdraw dataset to train and evaluate the model. The performance of the customized model is compared with various state-of-the-art deep learning algorithms like MobileNet and DenseNet architectures and proved that proposed model comparatively gives better results on Quickdraw dataset.",

author = "Nithin Deepkumar and Shilpa Ankalaki",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022 ; Conference date: 20-11-2022 Through 21-11-2022",

year = "2022",

doi = "10.1109/NKCon56289.2022.10126970",

language = "English",

series = "2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022",

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}

Deepkumar, N & Ankalaki, S 2022, A Simple Convolutional Neural Network for Doodle Classification. in 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022. 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022, Vijaypur, India, 20-11-22. https://doi.org/10.1109/NKCon56289.2022.10126970

A Simple Convolutional Neural Network for Doodle Classification. / Deepkumar, Nithin; Ankalaki, Shilpa.
2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Simple Convolutional Neural Network for Doodle Classification

AU - Deepkumar, Nithin

AU - Ankalaki, Shilpa

PY - 2022

Y1 - 2022

N2 - Visuals have been an integral part of our lives. The use of hand-drawn doodles is widespread and persistent, enabling people to retain information better and freely communicate thoughts and information. Doodle classification / hand-drawn diagram classification has significant application to communicate with persons who are deaf or hard of hearing that trains them to identify their urgent needs. The objective of this work is to develop a convolutional neural network (CNN) to classify the hand drawn images. This work utilized the Quickdraw dataset to train and evaluate the model. The performance of the customized model is compared with various state-of-the-art deep learning algorithms like MobileNet and DenseNet architectures and proved that proposed model comparatively gives better results on Quickdraw dataset.

AB - Visuals have been an integral part of our lives. The use of hand-drawn doodles is widespread and persistent, enabling people to retain information better and freely communicate thoughts and information. Doodle classification / hand-drawn diagram classification has significant application to communicate with persons who are deaf or hard of hearing that trains them to identify their urgent needs. The objective of this work is to develop a convolutional neural network (CNN) to classify the hand drawn images. This work utilized the Quickdraw dataset to train and evaluate the model. The performance of the customized model is compared with various state-of-the-art deep learning algorithms like MobileNet and DenseNet architectures and proved that proposed model comparatively gives better results on Quickdraw dataset.

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DO - 10.1109/NKCon56289.2022.10126970

M3 - Conference contribution

AN - SCOPUS:85163084807

T3 - 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

BT - 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

Y2 - 20 November 2022 through 21 November 2022

ER -

A Simple Convolutional Neural Network for Doodle Classification

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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