المجلة الجزائرية للعلوم
Volume 7, Numéro 14, Pages 165-170
2021-06-03
Handwritten Digit Recognition: Developing An Efficient Ml And Dl Model To Recognize Handwritten Digits
Authors : Tahmi Hassina . Said Gadri . Baya Loues . Nour El-houda Adouane . Nadjet Bouafia .
Abstract
Deep learning DL is a new subfield of machine learning ML area which is used during the last decades to develop more sophisticated algorithms allowing high performance in some popular recognition fields, such as: pattern recognition, computer vision and image classification. Among the most used methods in DL, we find CNNs (Convolutional Neural Networks) which can be considered as the best used technique. In the present work, we have developed an automatic classifier that permits to classify some given grayscale images representing handwritten digits into one of 10 classes (digits from 0 to 9), inclusively. For this purpose, we have used ML and DL approaches. First, we proceeded to the classification task using many ML algorithms including: LR, LDA, KNN, CART, NB, and SVM. Second, we proposed a new CNN model composed of many convolutional layers. Finally, we established a comparison between different algorithms.
Keywords
machine learning, deep learning, handwritten digit recognition, Pattern, Recognition, Neural Networks, Convolutional Neural Networks.
Les articles similaires
Boukrouh Mehdi
.
Lashab Mohamed
.
Goutas Ahcene
.
Ouchtati Salim
.
pages 1005-1017.
Dr. Bashir Bakri Agib Babiker
.
Dr. Ali Abbas Ali Awad Elseed
.
pages 221-239.
حراق سمية
.
لطرش ذهبية
.
ص 313-330.
Bouaziz Noureddine
.
pages 24-29.
