Abstract
In this paper, a new feature selection method is developed and investigated on handwritten digits classification task based on Discrete Cosine Transform (DCT). In the preprocessing stage, raw images are passed through normalization, slant filter, and edge-detection filter as well as the proposed DCT algorithm to reduce feature which can lead to sparse and orthonormal data matrix. The CNN classifier can inherently do feature extraction in additional to handwritten digit recognition. The proposed techniques of slant filter and DCT transformation along with the CNN classifier achieved the best global accuracy of 99.54%, 99.12%, and 98.52% for 784, 400, and 144 features, respectively on the public MNIST dataset of handwritten digits.
| Original language | English |
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| Title of host publication | Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020 |
| Editors | Anis Koubaa, Ahmad Taher Azar, Basit Qureshi |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 13-20 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781728174075 |
| DOIs | |
| State | Published - Nov 2020 |
Publication series
| Name | Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020 |
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Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- CNN
- Discrete Cosine Transform (DCT)
- Feature reduction
- deep learning
- slant and edge detection filters
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Instrumentation
- Computer Networks and Communications
- Artificial Intelligence
- Energy Engineering and Power Technology
- Control and Optimization