HANDWRITTEN NUMERICAL CHARACTER RECOGNITION

Handwritten Numerical Character Recognition (HNCR) is the process of interpreting handwritten digits by machines. There are several techniques in order to detect the handwritten digits. In this paper, it is proposed to the use of Histogram Orientated Gradient (HOG) feature extraction technique and S...

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Main Author: THEEBAN, PILLAI ANBALAGU
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20104 /
Published: IRC 2019
Online Access: http://utpedia.utp.edu.my/20104/1/Dissertation.pdf
http://utpedia.utp.edu.my/20104/
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recordtype eprints
spelling utp-utpedia.201042019-12-20T16:14:40Z http://utpedia.utp.edu.my/20104/ HANDWRITTEN NUMERICAL CHARACTER RECOGNITION THEEBAN, PILLAI ANBALAGU Handwritten Numerical Character Recognition (HNCR) is the process of interpreting handwritten digits by machines. There are several techniques in order to detect the handwritten digits. In this paper, it is proposed to the use of Histogram Orientated Gradient (HOG) feature extraction technique and Support Vector Machine (SVM) to detect the handwritten characters. HOG is a very efficient and stable feature in recognition system. Moreover, linear SVM has been employed as classifier which classify the handwritten characters with the help of Modified National Institute of Standard and Technology (MNIST) dataset with has a sample of 70,000. The combination of SVM and HOG is very efficient with MNIST dataset number classification. Moreover, the system is also test on 2 different single board computers to differentiate the performance of the 2 different systems. The primary scope of the project is to recognize and tabulate Student Exam Identity Number and Handwritten Marks. IRC 2019-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20104/1/Dissertation.pdf THEEBAN, PILLAI ANBALAGU (2019) HANDWRITTEN NUMERICAL CHARACTER RECOGNITION. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
description Handwritten Numerical Character Recognition (HNCR) is the process of interpreting handwritten digits by machines. There are several techniques in order to detect the handwritten digits. In this paper, it is proposed to the use of Histogram Orientated Gradient (HOG) feature extraction technique and Support Vector Machine (SVM) to detect the handwritten characters. HOG is a very efficient and stable feature in recognition system. Moreover, linear SVM has been employed as classifier which classify the handwritten characters with the help of Modified National Institute of Standard and Technology (MNIST) dataset with has a sample of 70,000. The combination of SVM and HOG is very efficient with MNIST dataset number classification. Moreover, the system is also test on 2 different single board computers to differentiate the performance of the 2 different systems. The primary scope of the project is to recognize and tabulate Student Exam Identity Number and Handwritten Marks.
format Final Year Project
author THEEBAN, PILLAI ANBALAGU
spellingShingle THEEBAN, PILLAI ANBALAGU
HANDWRITTEN NUMERICAL CHARACTER RECOGNITION
author_sort THEEBAN, PILLAI ANBALAGU
title HANDWRITTEN NUMERICAL CHARACTER RECOGNITION
title_short HANDWRITTEN NUMERICAL CHARACTER RECOGNITION
title_full HANDWRITTEN NUMERICAL CHARACTER RECOGNITION
title_fullStr HANDWRITTEN NUMERICAL CHARACTER RECOGNITION
title_full_unstemmed HANDWRITTEN NUMERICAL CHARACTER RECOGNITION
title_sort handwritten numerical character recognition
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20104/1/Dissertation.pdf
http://utpedia.utp.edu.my/20104/
_version_ 1741195578444873728
score 11.62408

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