Handwriting Character Recognition using Vector Quantization Technique

Haviluddin Haviluddin ((SCOPUS ID: 56596793000, Universitas Mulawarman))
Rayner Alfred
Ni’mah Moham
Herman Santoso Pakpahan
Islamiyah Islamiyah
Hario Jati Setyadi

Knowledge Engineering and Data Science
Vol 2, No 2 (2019)

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.