Handwritten Digit Recognition: Developing An Efficient Ml And Dl Model To Recognize Handwritten Digits

Résumé: 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.

Publié dans la revue: المجلة الجزائرية للعلوم