International audienceRecognition of handwritten digits has been one of the first applications of neural networks. Efficient methods have already been proposed to solve this task. We propose an intermediate approach between classical methods, which are based on extraction of a small set of parameters, and pure neural methods,in which the neural network is fed with raw image data. Complexity and learning time are reduced with still good performances. On a data base of 2589 digits coming from 30 people, we provide experimental results and comparisons of various parameters and classifiers
Recognition of handwritten digits/characters is an important and necessary step in many documents pr...
The wide range of shape variations for handwritten digits requires an adequate representation of the...
© 2020 IEEE. The use of a combination of a convolutional neural network and multilayer perceptrons f...
In this paper we present a method for the recognition of handwritten digits and a practical implemen...
A handwritten digit recognition system was used in a demonstration project to visualize artificial n...
We show that neural network classifiers with single-layer training can be applied efficiently to com...
Abstract- In this paper, we present a new neural network based method for handwritten character reco...
International audienceHandwritten digits recognition has been widely studied because of its potentia...
The aim of this paper is to implement a Multilayer Perceptron (MLP) Neural Network to recognize and ...
The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where da...
Abstract. In this paper, results of an experimental study of a deep con-volution neural network arch...
D.Ing. (Electrical and Electronic )This thesis describes a neural network based system for the class...
D.Ing. (Electrical and Electronic )This thesis describes a neural network based system for the class...
Technological development in recent years has generated the constant need to digitalize and analyze ...
Project deals with the applications of ML (Machine Learning ) techniques for detecting Hand written ...
Recognition of handwritten digits/characters is an important and necessary step in many documents pr...
The wide range of shape variations for handwritten digits requires an adequate representation of the...
© 2020 IEEE. The use of a combination of a convolutional neural network and multilayer perceptrons f...
In this paper we present a method for the recognition of handwritten digits and a practical implemen...
A handwritten digit recognition system was used in a demonstration project to visualize artificial n...
We show that neural network classifiers with single-layer training can be applied efficiently to com...
Abstract- In this paper, we present a new neural network based method for handwritten character reco...
International audienceHandwritten digits recognition has been widely studied because of its potentia...
The aim of this paper is to implement a Multilayer Perceptron (MLP) Neural Network to recognize and ...
The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where da...
Abstract. In this paper, results of an experimental study of a deep con-volution neural network arch...
D.Ing. (Electrical and Electronic )This thesis describes a neural network based system for the class...
D.Ing. (Electrical and Electronic )This thesis describes a neural network based system for the class...
Technological development in recent years has generated the constant need to digitalize and analyze ...
Project deals with the applications of ML (Machine Learning ) techniques for detecting Hand written ...
Recognition of handwritten digits/characters is an important and necessary step in many documents pr...
The wide range of shape variations for handwritten digits requires an adequate representation of the...
© 2020 IEEE. The use of a combination of a convolutional neural network and multilayer perceptrons f...