Postal Code Handwritten Recognition System
| dc.contributor.author | Amroune, Abdelheq | |
| dc.date.accessioned | 2018-02-26T08:04:36Z | |
| dc.date.available | 2018-02-26T08:04:36Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | A three based classifiers system was created. A back-propagation neural network with one hidden layer , a support Vector machine classifier, and K- nearest Neighbor classifier were used to create an adaptive postal code digits recognition system by using the Hu moments invariants feature extraction method. The system was trained and evaluated through different forms of handwriting samples provided by both male and female participants. Experiments tested, the effect of the size set on the recognition accuracy, and the effect of handwriting style on the recognition accuracy. Results showed that the handwriting style of the subjects had | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/3159 | |
| dc.language.iso | en | en_US |
| dc.publisher | Faculty of Mathematics and Informatics - Unviversity Mohamed MOHAMED - M’SILA | en_US |
| dc.subject | KNN, SVM, ANN, Hu moments ,code postal, recognition system. | en_US |
| dc.title | Postal Code Handwritten Recognition System | en_US |
| dc.type | Thesis | en_US |