CAJ | 학술논문

提出了一种能有效完成对无监督字符分类的模糊逻辑方法,以提高字符识别系统的速度,正确性和鲁棒性.字符首先被分为8种印刷结构类,然后采用模式匹配方法将各类字符分别转换成基于一非线性加权相似函数的模糊样板集合.模糊无监督字符的分类是字符匹配的一种自然范例并发展了加权模糊相似测量的研究.本文讨论了该模糊模型的特性并用以加快字符分类处理,经过字符分类,在字符识别时由于只需针对较小的模糊样板集合而变得容易和快速.
제출료일충능유효완성대무감독자부분류적모호라집방법,이제고자부식별계통적속도,정학성화로봉성.자부수선피분위8충인쇄결구류,연후채용모식필배방법장각류자부분별전환성기우일비선성가권상사함수적모호양판집합.모호무감독자부적분류시자부필배적일충자연범례병발전료가권모호상사측량적연구.본문토론료해모호모형적특성병용이가쾌자부분류처리,경과자부분류,재자부식별시유우지수침대교소적모호양판집합이변득용역화쾌속.
This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the representation of prototypes for character matching, is developed and a weighted fuzzy similarity measure is explored.The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character recognition which is simply applied on a smaller set of the fuzzy prototypes, becomes much easier and less time-consuming.