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Handwritten Digit Recognition using Image Preprocessing and CNN

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  • Anshul Dubey
  • Ashley Lazarus
  • Dharmendra Mangal

Abstract

Handwritten digit recognition, is a technique of identifying and enlisting the recognized digit, that uses neural networks, deep learning and machine learning. The applications and demand of handwritten digit recognition systems such as zip code recognition, car number plate recognition, robotics, banks, mobile applications and numerous more, are soaring every day. It can be done through numerous approaches, but convolutional neural network is considered one of the best methods. The special neural network uses multilayer architecture for identification and classification. Although the accuracy factor can be increased, based on image preprocessing, in this paper we discuss how the accuracy of the system can be increased for better handwritten digit recognition, using convolutional neural networks, image preprocessing; binarization, resizing, rotation. The accuracy rate obtained is 99.33%.

Suggested Citation

  • Anshul Dubey & Ashley Lazarus & Dharmendra Mangal, 2020. "Handwritten Digit Recognition using Image Preprocessing and CNN," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 6(3), pages 896-902, June.
    • Handle: RePEc:jbh:ijsrcs:v6:y2020:i3:id:hcseit206396
    DOI: 10.32628/CSEIT206396
    Note: Article URL: https://ijsrcseit.com/CSEIT206396
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