Handwritten digits recognition with decision tree classification: a machine learning approach

Tsehay Admassu Assegie, Pramod Sekharan Nair

Abstract


Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

Keywords


decision tree classification; handwritten digits; handwritten digits recognition; machine learning;

Full Text:

PDF


DOI: http://doi.org/10.11591/ijece.v9i5.pp4446-4451

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578