Hand Written Digit Recognition using Machine Learning

被引:0
作者
Sethi, Rohan [1 ]
Kaushik, Ila [2 ]
机构
[1] HMR Inst Technol & Management, Dept Comp Sci & Engn, Delhi, India
[2] Krishna Inst Engn & Technol, Dept Informat Technol, Ghaziabad, India
来源
2020 IEEE 9TH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORK TECHNOLOGIES (CSNT 2020) | 2020年
关键词
KNN; OCR; Naive Bayes; Neural Networks; Feature extraction; Optical character recognition; Segmentation; Handwritten digit classification;
D O I
10.1109/csnt48778.2020.9115746
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. SVM is applied for recognition and classification, while Convex hull algorithm is applied for feature extraction.
引用
收藏
页码:49 / 54
页数:6
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