International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022
p-ISSN: 2395-0072
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www.irjet.net
HANDWRITTEN DIGIT RECOGNITION Dhruv Sharma1, Ishaan Singh2, Upendra Pandey3
1,2 Student,
Computer Engineering Dept, Delhi Technical Campus, Greater Noida, Uttar Pradesh, India Computer Science Dept, Delhi Technical Campus, Greater Noida, Uttar Pradesh, India --------------------------------------------------------------------***--------------------------------------------------------------------Abstract – “The handwritten digit recognition problem becomes one of the most notorious problems in machine” “literacy and 3 Professor,
computer vision operations. numerous machine literacy ways have been employed to break the” “handwritten number recognition problem. This paper focuses on Neural Network( NN) approaches. The three most” “popular NN approaches are deep neural network( DNN), deep belief network( DBN) and convolutional neural “network( CNN). In this paper, the three NN approaches are compared and estimated in terms of numerous factors” “similar as delicacy and performance. Recognition delicacy rate and performance, still, isn't the only criterion in the" “evaluation process, but there are intriguing criteria similar as prosecution time. Random and standard dataset of” “handwritten number have been used for conducting the trials. The results show that among the three NN approaches,” “DNN is the most accurate algorithm; it has98.08 delicacy rate. still, the prosecution time of DNN is similar with the “other two algorithms.”
Key Words: Handwritten Digit Recognition; Convolutional Neural Network ; Random and Standard dataset; Accuracy 1. INTRODUCTION “The handwritten digit recognition is the ability to recognize human handwritten digits by computers. It is” “considered to be a hard task for the machine because handwritten digits are not perfect and can be made with” “many different techniques. A solution to this problem is handwritten digit recognition which uses the image of a” “digit and thereby recognizes the digit present in it.”
“Handwritten digit recognition is widely used in the field of automatic processing of bank cheques, postal” “addresses etc. Some of the existing systems also include computational intelligence techniques such as artificial” “neural networks while others may be just large lookup tables.” “Although the artificial neural networks had been developed since 1940s but they have been widely applied to a” “large variety of disciplines only since the past fifteen years.” “Originating from the simple mathematical model of a biological neuron i.e. an artificial neuron, many varieties of” “neural networks exist these days. Although some are implemented in hardware but the majority are always” “simulated in software.”
Figure1: Handwritten Digits “Artificial neural nets have been successfully applied to handwritten digit recognition numerous times, with very” “small margins of errors.”
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