Automated Math Symbol Classification Using SVM
Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become...
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| Published in | International journal of e-collaboration Vol. 18; no. 2; pp. 1 - 14 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hershey
IGI Global
01.01.2022
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| Subjects | |
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| Abstract | Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy. |
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| AbstractList | Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy. Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is recognizing the handwritten mathematical symbol, digits, and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation, and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy. |
| Audience | Academic |
| Author | Vaidehi K Manivannan R |
| AuthorAffiliation | Stanley College of Engineering and Technology for Women, India |
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| Cites_doi | 10.1109/ICSCN.2007.350685 10.1109/ICASSP.2008.4518019 10.5120/14545-2644 10.1145/3306500.3306543 10.1109/ICECTECH.2011.5941699 10.5120/10029-5012 10.5120/8114-1695 10.1016/j.patrec.2008.02.005 10.1109/TPAMI.2016.2572693 10.1109/ICDAR.2011.297 10.5120/1236-1693 10.1080/03772063.2017.1351323 10.1109/ICICIC.2006.506 10.1109/ICoICT.2017.8074699 10.1109/ACPR.2015.7486478 10.1016/j.patcog.2015.02.017 10.1109/ICFHR.2014.61 10.1109/ICDAR.2011.98 10.1109/TIP.2012.2199328 10.1109/CCIntelS.2015.7437901 |
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| Copyright | COPYRIGHT 2022 IGI Global Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. |
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| References | G.Kumar (IJeC.304037-16) 2013; 3 R. L.Kulkarni (IJeC.304037-24) 2017; 5 IJeC.304037-20 IJeC.304037-11 IJeC.304037-13 IJeC.304037-12 IJeC.304037-15 IJeC.304037-14 IJeC.304037-17 IJeC.304037-19 G.Kumar (IJeC.304037-4) 2013; 3 IJeC.304037-6 IJeC.304037-5 IJeC.304037-8 IJeC.304037-7 IJeC.304037-2 IJeC.304037-1 IJeC.304037-3 IJeC.304037-9 R. L.Das (IJeC.304037-18) 2012; 46 IJeC.304037-22 IJeC.304037-21 IJeC.304037-23 IJeC.304037-26 L.Perez (IJeC.304037-10) 2017 IJeC.304037-25 IJeC.304037-28 IJeC.304037-27 IJeC.304037-0 |
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| SubjectTerms | Accuracy Analysis Automation Character recognition Classification Collaboration Employee motivation Feature extraction Fourier transforms Handwriting Handwriting recognition Mathematical analysis Neural networks Support vector machines |
| Title | Automated Math Symbol Classification Using SVM |
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