• E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Leveraging the CNN (Convolutional Neural Network) in the Effective Recognition of the Hand Written Digits

Somya

74 - 76 Vol. 7, Jan-Dec, 2021
Receiving Date: 2021-03-04;    Acceptance Date: 2021-05-17;    Publication Date: 2021-05-25
Download PDF

Abstract

Digit recognition is an intriguing and huge theme. It is a way to perceive and arrange digits that have been transcribed. It can be used for programmed bank checks, postal addresses, and tax paperwork. Since the physically composed digits are not overall similar in size, thickness, position, and heading, various hindrances should be considered to decide the issue of transcribed digit acknowledgement. The diversity and distinctiveness of various compositional styles also influence the appearance and presence of the digits.

Keywords: digit recognition; CNN (Convolutional Neural Network); Hand written digits

    References

  1. Handwritten Digit Classification Using the MNIST Dataset, M. Wu and Z. Zhang, 2010.
  2. Dutta, A., and Dutta, A., Handwritten digit recognition Utilizing deep learning, July 2017, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 6, no. 7.
  3. Al Maadeed, Somaya, and Abdelaali Hassaine, Automatic age, gender, and nationality prediction in offline handwriting. EURASIP Journal on Image and Video Processing, Volume 1, Issue 1, 2014.
  4. Gaurav Jain and Jason Ko, Handwritten Digits Recognition, University of Toronto Project Report, 11/21/2008.
  5. Handwritten recognition using SVM, KNN, and neural network, arXiv preprint arXiv:1702.00723 (2017), Hamid, Norhidayu Abdul, and NilamNur AmirSjarif.
  6. Handwritten digit categorization using support vector machines, R.G.Mihalyi, 2011.
  7. Ishani Patel.Virag Jagtap, Ompriya Kale ,“A Survey on Feature Extraction Methods for Handwritten Digits Recognition”, IJCA (0975 – 8887), Volume 107 –No 12, Dec (2015).
  8. Reena Bajaj, Lipika Dey, and S. Chaudhury, “Devnagari numeral recognition combining decision of multiple connectionist classifiers”, Sadhana, Vol.27, part.1, pp.-59-72, 2011.
  9. Handwritten Digit Recognition Using Deep Learning”, Anuj Dutt and Aashi Dutt
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