Dynamic network for identification of character locations using self-organized clustering

✍ Scribed by Masanori Kondo; Yoshikazu Miyanaga; Koji Tochinai

• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 831 KB
• Volume: 79
• Category: Article
• ISSN: 1042-0967
• DOI: 10.1002/ecjc.4430790702

✦ Synopsis

This paper proposes a new network that evaluates the locations of targets, i.e., target characters, and recognizes them in an image mixed with other objects, i.e., noncharacters. This network consists of a self-organized clustering layer, a simple p e q t r o n layer, and a controller. The purpose of the self-organized clustering is to evaluate the stochastic characteristics of an input data set; the self-organization is executed by calculating the distance between stochastic characteristics in this layer and the stochastic characteristics of an input vector. The simple perceptron layer determines the location of a target character and simultaneously recognizes its character by linearly dividing all outputs given from the selforganized clustering layer. In the proposed system, the location identification and its recognition of a target character can be adaptively performed by using two small different compensative networks. The location identification and its recognition of real target characters, which are obtained in printed boards, are also shown in this paper.