UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2107649


Registration ID:
313131

Page Number

f145-f164

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Title

Handwritten Digit Classification

Abstract

In the current age of digitization, handwriting recognition plays an important role in information processing. There is a lot of information available on paper and less processing of digital files than the processing of traditional paper files. The purpose of the handwriting recognition system is to convert handwritten letters into machine-readable formats. Major applications include vehicle license-plate identification, postal paper-sorting services, historical document preservation in the check truncation system (CTS) scanning and archaeology departments, old document automation in libraries and banks, and more. All of these areas deal with large databases and therefore require high identification accuracy, low computational complexity, and consistent performance of the identification system. Over time, the number of fields that can implement deep learning is increasing. In deep learning, convolutional neural networks (CNN) are being used for visual image analysis. CNN can be used in object detection, facial recognition, robotics, video analysis, segmentation, pattern recognition, natural language processing, spam detection, topical gradation, regression analysis, speech recognition, image classification. Detection of handwritten numbers, including accuracy in these areas, has reached human perfection using deep convolutional neural networks (CNNs). Recently CNN has become one of the most attractive approaches and has been the ultimate factor in recent success and in several challenging machine learning applications. Considering all the factors stated above we have chosen CNN for our challenging tasks of image classification. We can use it to identify handwritten numbers, which is one of the higher education and business transactions. There are many applications of handwritten digit recognition for our real-life purposes. Hence we are using the Convolutional Neural Network (CNN) and MNIST dataset.

Key Words

Convolutional Neural Networks(CNN),Deep Learning,MNIST Dataset,Handwritten Digit Recognition,TensorFlow & Keras.

Cite This Article

"Handwritten Digit Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.f145-f164, July-2021, Available :http://www.jetir.org/papers/JETIR2107649.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Handwritten Digit Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppf145-f164, July-2021, Available at : http://www.jetir.org/papers/JETIR2107649.pdf

Publication Details

Published Paper ID: JETIR2107649
Registration ID: 313131
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: f145-f164
Country: FARIDABAD, Haryana, India .
Area: Engineering
ISSN Number: 2349-5162


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