Abstract
The aim of this paper is to analyze efficiency of three classifiers which will be experimented and compared to find out the best techniques. They were experimented on a standard database of handwritten digit. However, not only recognition rate is considered, but also other issues (ex. error rate, misclassified image rate and computing time) will be analyzed. The presented results show that SVM is the best classifier to recognize handwritten digits. That is, the highest recognition rates (96.93%) are obtained. But the computing time of training is the main problem for them. Conversely, other methods, like neural networks, give insignificantly worse results, but their training is much quicker. However, all of the techniques also represent an error rate of 1–4% because of confusionwithdigits 1 and 7 or 3, 5 and 8 respectively.
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Kaensar, C. (2013). A Comparative Study on Handwriting Digit Recognition Classifier Using Neural Network, Support Vector Machine and K-Nearest Neighbor. In: Meesad, P., Unger, H., Boonkrong, S. (eds) The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Advances in Intelligent Systems and Computing, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37371-8_19
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DOI: https://doi.org/10.1007/978-3-642-37371-8_19
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