Authors:
Bing Quan Huang
and
Tahar Kechadi
Affiliation:
University College Dublin, Ireland
Keyword(s):
Recurrent neural network, gradient descent method, learning algorithms, Markov chain model, fuzzy logic, feature extraction, context layers.
Related
Ontology
Subjects/Areas/Topics:
Advanced Applications of Fuzzy Logic
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of Artificial Intelligence
;
Machine Perception: Vision, Speech, Other
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper presents an innovative hybrid approach for online recognition of handwritten symbols. This approach is composed of two main techniques. The first technique, based on fuzzy logic, deals with feature extraction from a handwritten stroke and the second technique, a recurrent neural network, uses the features as an input to recognise the symbol. In this paper we mainly focuss our study on the second technique. We proposed a new recurrent neural network architecture associated with an efficient learning algorithm. We describe the network and explain the relationship between the network and the Markov chains. Finally, we implemented the approach and tested it using benchmark datasets extracted from the Unipen database.