Dilmi, AnesLarbaoui, Ayoub Fateh AllahSupervisor: Bentrcia, Rahima2022-07-212022-07-212022-06-10http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/30889The main purpose of this thesis is to build a method for automatic recognition of handwritten digits. To accomplish the recognition task, first, the data set is presented as input, this is followed by a preprocessing phase, where the image undergoes various operations such as noise reduction, normalization, smoothing, and skeletonization. The result of this stage can be given as input to the stage of automatically extracting feature maps from images using 3 layers of Convolutional Neural Networks (CNN), and then fed to 4 layers of FC ANN, where these images are classified into ten classes (0 to 9). The accuracy of the final model is important because more accurate models make better decisions. Our results showed a classification accuracy of 99.78% and a loss value of 1.82% using the MNIST data set.enConvolutional Neural Networks; MNIST; features extraction; preprocessing; Artificial Neural Networks.Thesis