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Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition
| Content Provider | Scilit |
|---|---|
| Author | Popko, E. A. Weinstein, I. A. |
| Copyright Year | 2016 |
| Description | Journal: Journal of Physics: Conference Series Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved. |
| Related Links | http://iopscience.iop.org/article/10.1088/1742-6596/738/1/012123/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/738/1/012123 |
| Journal | Journal of Physics: Conference Series |
| Volume Number | 738 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2016-08-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Hardware and Architecture Convolutional Neural Network Fuzzy Logic Pattern Recognition Logic Module Handwritten Digits |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
