| Improving Persian Digit Recognition by Combining Deep Neural Networks and SVM and Using PCA |
| Paper ID : 1020-MVIP2020 |
| Authors: |
|
AmirMahmood MousaviHarris *1, Alireza Bossaghzadeh2 1none,none 2Department of Computer Engineering Shahid Rajee University |
| Abstract: |
| One of the machine vision tasks is optical character recognition (OCR) that researchers in this field are trying to achieve a high performance and accuracy in the classification task. In this paper, we have used a fine tuned deep Neural networks for Hoda dataset, which is the largest dataset for Persian handwritten digit classification, to extract valuable discriminative features. then, these features are fed to a linear support vector machine (SVM) for classification part. In the next experiment, In order to improve the accuracy and computational load, we applied the Principal component analysis (PCA) to reduce the extracted features dimensions then we fed it to SVM. To the best of our knowledge the proposed method was better than other methods in terms of accuracy measure. |
| Keywords: |
| Deep neural networks, Computer Vision, VGG16, OCR, SVM, PCA, feature extraction |
| Status : Paper Accepted (Oral Presentation) |
