Recognition Of Kalbar Dunging Script Patterns using The Learning Vector Quantization (Lvq) Method
Abstract
This article implements the Learning Vector Quantization (LVQ) method in recognizing Dunging character patterns. In this case, it is a system that groups dunging characters according to their respective classes. By applying these two theories, the system will identify handwritten dunging script which has characteristics resembling human neural networks. The data used is in the form of images that have been taken using digital camera photos and smartphones.
The image is converted into numeric using image processing. The image processing stages include the process of cropping the RGB image to 50 x 50 pixels, and image binarization. The network training stage uses data of 10 images consisting of 10 characters of Dunging script. The results of testing the training image obtained a percentage of 88.66%. Using dunging child diacritics, using or adding some image processing functions, and implemented. From the accuracy results obtained, it can be said that the LVQ method is not optimal in solving pattern recognition problems, especially Dunging characters. Optimization techniques for the LVQ learning process with optimization algorithms are the next research plan.
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