Recognition of Handwritten Digit using Convolutional Neural Network (CNN)

Recognition of Handwritten Digit using Convolutional Neural Network (CNN)

Md. Anwar Hossain

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Md. Mohon Ali

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α Pabna University of Science and Technology

Recognition of Handwritten Digit using Convolutional Neural Network (CNN)

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Abstract

Humans can see and visually sense the world around them by using their eyes and brains. Computer vision works on enabling computers to see and process images in the same way that human vision does. Several algorithms developed in the area of computer vision to recognize images. The goal of our work will be to create a model that will be able to identify and determine the handwritten digit from its image with better accuracy. We aim to complete this by using the concepts of Convolutional Neural Network and MNIST dataset. We will also show how MatConvNet can be used to implement our model with CPU training as well as less training time. Though the goal is to create a model which can recognize the digits, we can extend it for letters and then a person’s handwriting. Through this work, we aim to learn and practically apply the concepts of Convolutional Neural Networks.

References

17 Cites in Article

Reference Format

Nimisha Jain,Kumar Rahul,Ipshita Khamaru,Anupam Jha,Ghosh (2017). HAND WRITTEN DIGIT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK FOR FPGA.
Dr (2016). A Comprehensive Review On Handwritten Digit Recognition Using Various Neural Network Approaches.
Al-Mansoori Saeed (2015). Intelligent Handwritten Digit Recognition using Artificial Neural Network.
Nurul Ilmi,W Budi,R Nur (2016). Handwriting digit recognition using local binary pattern variance and K-Nearest Neighbor classification.
Tobias Kutzner,Mario Dietze,Ingrid Bonninger,Carlos Travieso,Malay Dutta,Anushikha Singh (2016). Online handwriting verification with safe password and increasing number of features.
He Kaiming,Xiangyu,Zhang,Ren Shaoqing,Jian Sun Spatial pyramid pooling in deep convolutional networks for visual recognition‖ European.
Ernst Kussul,Tatiana Baidyk (2004). Improved method of handwritten digit recognition tested on MNIST database.
Md. Hossain,Md. Ali (2014). Recognition of Handwritten Digit using Convolutional Neural Network (CNN).
Gil Levi,Tal Hassner (2013). OFFLINE HANDWRITTEN DIGIT RECOGNITION USING NEURAL NETWORK.
Jing Xuanyang,Pu MDig: Multi-digit Recognition using Convolutional Neural Network on Mobile.
Yuhao Zhang Deep Convolutional Network for Handwritten Chinese Character Recognition.
In-Jung Kim,Xiaohui Xie (2014). Handwritten Hangul recognition using deep convolutional neural networks.
Nada Basit,Yutong Zhang,Hao Wu,Haoran Liu,Jieming Bin,Yijun He,Abdeltawab Hendawi (2016). MapReduce-based deep learning with handwritten digit recognition case study.
Fabien Lauer,Ching Suen,Gérard Bloch (2007). A trainable feature extractor for handwritten digit recognition.
Alex Krizhevsky,Ilya Sutskever,Geoffrey Hinton (2012). ImageNet classification with deep convolutional neural networks.
Cheng-Lin Liu,Kazuki Nakashima,Hiroshi Sako,Hiromichi Fujisawa (2003). Handwritten digit recognition: benchmarking of state-of-the-art techniques.
Matthew Zeiler,Rob Fergus (2013). Visualizing and Understanding Convolutional Networks.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Md. Anwar Hossain. 2019. \u201cRecognition of Handwritten Digit using Convolutional Neural Network (CNN)\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D2): .

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GJCST Volume 19 Issue D2
Pg. 27- 33

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification: I.2.6

Submission ReceivedDecember 15, 2018
Peer Review Double Blind
Handling Editor
Accepted January 3, 2019
Published January 15, 2019

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v1.2

Issue date

May 18, 2019

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Article Matrices

Total Score: 102

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology - D: Neural & AI

Authors: Md. Anwar Hossain, Md. Mohon Ali (PhD/Dr. count: 0)

View Count (all-time): 235

Total Views (Real + Logic): 5148

Total Downloads (simulated): 1321

Publish Date: 2019 05, Sat

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Total Views: 5148

Total Downloads: 1321

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