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  Published Paper Details:

  Paper Title

HAND WRITTEN DIGIT RECOGNITION USING CONVOUTION NEURAL NETWORK

  Authors

  Ms. Punya M.P,  Ms. Baby,  Prof. Lokesh B

  Keywords

Convolution, Padding, Stride, Softmax, Maxpooling, ReLU

  Abstract


Digit recognition is a suitable model issue for learning about the Neural Networks, and it will pave the way for sophisticated Deep Learning techniques.Manymethods are found in literature to recognize and classify the digits which are written by hand (handwritten digits).ThispaperexplainshowtorecognizeandclassifyhandwrittendigitsusingCNNandMATLABforenhancedperformance. A deep convolutional neural network (CNN) is a type of neural network that is used to recognise images. This method determines how important the deep layer improvements are for processingthe image byusing MATLAB, we provide the implementationof necessary for constructing and applying Convolution Neural Network to a high-quality data set known as MNIST which is a collection of more than 60,000 handwritten digits dataset for training purpose and 10,000 digitsdatasetfortesting purpose.Whentestedthedevelopedmodelforclassification, we got 99.60% accuracy and prove to be betterthanotherclassifier. Many hidden convolution layers and more hidden artificial neurons could improve the accuracy of the outcome.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTT020018

  Paper ID - 223629

  Page Number(s) - 87-91

  Pubished in - Volume 10 | Issue 8 | August 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Ms. Punya M.P,  Ms. Baby,  Prof. Lokesh B,   "HAND WRITTEN DIGIT RECOGNITION USING CONVOUTION NEURAL NETWORK", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 8, pp.87-91, August 2022, Available at :http://www.ijcrt.org/papers/IJCRTT020018.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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