@thesis{thesis,
		author={Azizi  Izyan},
		title ={Optical Character Recognition Menggunakan Learning Vector Quantization Pada Dokumen Karya Tulis Ilmiah},
		year={2020},
		url={http://elibrary.unikom.ac.id/id/eprint/4177/},
		abstract={Optical Character Recognition or OCR is a technology that used for character recognition or object in image. OCR has been used in various type of research as Arabic handwriting, sundanese script handwriting, title of novel with the accuracy results obtained each for 82,1%, 78,67% and 55,62. In this research, OCR will be done using Learning Vector Quantization method, Learning Vector Quantization has been used for OCR in Arabic handwriting, and continued handwriting. Focus in this research is to measure the level of accuracy produces by LVQ and zoning feature extraction in recognition the character contained in the scanned image of scientific papers. Before LVQ training and testing, the image through the preprocessing stage, such as grayscalling, binaryization, thresholding, line segmentation, word segmentation, character segmentation and extraction of zoning features, from the result of the preprocessing stage is obtained value of image characteristic for LVQ training and testing. From the test conducted on 30 abstract scan image documents, the highest accuracy is 10,08% with the parameter number of learning rate 0,05 and epoch 200 and the lowest accuracy is 2,49 % with the parameter number of learning rate 0,1 and epoch 200. The low result of accuracy is influenced by segmentation  process that has not been able to overcome the attached character, and influenced by the selection of zones during the zoning feature extraction stage.}
		}