Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition
Abstract
This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods.
Keywords
Handwritten digit recognition; MLP; OCR; Random forests; SVM
Full Text:
PDFDOI: http://doi.org/10.11591/ijeecs.v21.i1.pp574-581
Refbacks
- Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition
- —
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).