Back-Propagation Neural Networks System for Recogniting Japanese Character

• Main Authors: LIN,TZU-YANG, 林子揚
• Other Authors: HUANG,CHU-HUANG
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/51868533944161090993

碩士 === 國立高雄海洋科技大學 === 電訊工程研究所 === 104 === Thanks to the advancement of technology, neural networks can be used widely and in different aspects. They are found everywhere in life but are most commonly used to predict, identify, classify and self-learn. In this study, backpropagation neural network is used for word identification in Japanese more specifically identifying Japanese fonts shown in different ways correctly. Nowadays, it is a globalized society, and language plays an important role in our life. Unfortunately, not every one of us is familiar to the languages of each country and thus we rely on metaphorical bridges which are called translators to communicate with people from different countries. However, as for the aspects related to business and communication, paperwork is always needed. Since everyone’s handwriting is distinctive, the main point discussed in the study is then how to identify certain unclear fonts. A backpropagation network is used to do symbol identification in this study. It is designed to identify Japanese Katakana by getting imaging systems to digitize each Japanese sound and collect them. They are categorized by visual range of the system using a five by seven boolean lattice to represent each sound. However, imaging systems are not complete enough and each sound may be interfered by the noise, so the program is designed to perfectly classify the ideal input vectors and reasonably and correctly classify the noise vectors. Keywords: Backpropagation Neural Network, MATLAB, font identification