Abstract
The aim of this paper is to use some feature extraction methods for the recognition of handwriting digits. The paper presents a hybrid approach based on the Multi Layer Perceptron (MLP) neural network and the Hidden Markov Model (HMM), Initially, the learning for each recognition system is used by a 2000 images database where the MLP neural network is based on the gradient back-propagation learning algorithm, and the HMM system is based on the Baum-Welch back-propagation learning algorithm. Finally, the recognized digits obtained by both systems are summed for the calculation of the recognition rate. The obtained results using the proposed approach have attained an impressive recognition rate of 98.8%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
