Russian Character Recognition using Self-Organizing Map

International Conference on Computing and Applied Informatics 2016

物理学;计算机科学

Gunawan, D.^1 ; Arisandi, D.^1 ; Ginting, F.M.^1 ; Rahmat, R.F.^1 ; Amalia, A.^2

Department of Information Technology, Faculty of Computer Science and Information Technology, University of Sumatera Utara, Kampus USU, Jl. dr. Mansur No. 9, Medan

20155, Indonesia^1

Department of Computer Science, Faculty of Computer Science and Information Technology, University of Sumatera Utara, Kampus USU, Jl. dr. Mansur No. 9, Medan

20155, Indonesia^2

关键词: Capture images;    Computer-generated images;    Different shapes;    Digital dictionaries;    Noise filtering;    Pre-processing step;    Word recognition;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012040/pdf
DOI  :  10.1088/1742-6596/801/1/012040

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The World Tourism Organization (UNWTO) in 2014 released that there are 28 million visitors who visit Russia. Most of the visitors might have problem in typing Russian word when using digital dictionary. This is caused by the letters, called Cyrillic that used by the Russian and the countries around it, have different shape than Latin letters. The visitors might not familiar with Cyrillic. This research proposes an alternative way to input the Cyrillic words. Instead of typing the Cyrillic words directly, camera can be used to capture image of the words as input. The captured image is cropped, then several pre-processing steps are applied such as noise filtering, binary image processing, segmentation and thinning. Next, the feature extraction process is applied to the image. Cyrillic letters recognition in the image is done by utilizing Self-Organizing Map (SOM) algorithm. SOM successfully recognizes 89.09% Cyrillic letters from the computer-generated images. On the other hand, SOM successfully recognizes 88.89% Cyrillic letters from the image captured by the smartphone's camera. For the word recognition, SOM successfully recognized 292 words and partially recognized 58 words from the image captured by the smartphone's camera. Therefore, the accuracy of the word recognition using SOM is 83.42%