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.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- A Comparative Study on Handwriting Digit Recognition Classifier Using Neural Network, Support Vector Machine and K-Nearest Neighbor
- Springer Berlin Heidelberg
Neuer Inhalt/© ITandMEDIA