<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Derdour, Khedidja</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Bensaadi, Rafik</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal on Electrical Engineering and Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.proquest.com/openview/72acbcf9bceca23bc38c7d1df8734acb/1?pq-origsite=gscholar&amp;cbl=316223</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we present a system for handwriting digit recognition using different invariant features extraction and multiple classifiers. In the feature extraction we use four types: cavities, Zernike moments, Hu moments, Histogram of Gradient (HOG). Firstly, the features are used independently by five classifiers: K-nearest neighbor (KNN), Support Vector Machines (SVM) one versus one, SVM one versus all, Decision Tree, MLP. Then to achieve the best possible classification performance in terms of recognition rate, three methods of classifiers Combination rule employed: majority vote, Borda count and maximum rule. Experiments are performed on the well-known MNIST database of handwritten digits. The results demonstrated that the combination of KNN using HOG features with SVMOVA using Zernike moments by Borda count rule have considered to be good based on a geometric transformation invariance.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>