skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Sensors (Basel, Switzerland), 2020-06, Vol.20 (12), p.3344 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20123344 ;PMID: 32545702

Full text available

Citations Cited by
  • Title:
    Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)
  • Author: Ahlawat, Savita ; Choudhary, Amit ; Nayyar, Anand ; Singh, Saurabh ; Yoon, Byungun
  • Subjects: Accuracy ; Algorithms ; Architecture ; Artificial neural networks ; Classification ; Complexity ; Computing costs ; convolutional neural networks ; Datasets ; Dilution ; Experiments ; Feature extraction ; Handwriting ; Handwriting recognition ; handwritten digit recognition ; Machine learning ; Neural networks ; OCR ; Optical character recognition ; Optimization ; pre-processing ; Regularization methods
  • Is Part Of: Sensors (Basel, Switzerland), 2020-06, Vol.20 (12), p.3344
  • Description: Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s20123344
    PMID: 32545702
  • Source: GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait