Combination of Convolutional Neural Network Architecture and its Learning Method for Rotation‐Invariant Handwritten Digit Recognition

2020 ◽  
Vol 16 (1) ◽  
pp. 161-163
Author(s):  
Kazuya Urazoe ◽  
Nobutaka Kuroki ◽  
Tetsuya Hirose ◽  
Masahiro Numa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Learning Method ◽  
Neural Network Architecture ◽  
Rotation Invariant ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

Morphological Convolutional Neural Network Architecture for Digit Recognition

2019 ◽  
Vol 30 (9) ◽  
pp. 2876-2885 ◽  
Author(s):  
Dorra Mellouli ◽  
Tarek M. Hamdani ◽  
Javier J. Sanchez-Medina ◽  
Mounir Ben Ayed ◽  
Adel M. Alimi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Digit Recognition

scholarly journals An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

2019 ◽  
Vol 1 (9) ◽  
Author(s):  
Saqib Ali ◽  
Zeeshan Shaukat ◽  
Muhammad Azeem ◽  
Zareen Sakhawat ◽  
Tariq Mahmood ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit
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