Vision technique for the recognition of billet characters in the steel plant

Abstract

This paper describes vision technique about the real time billet characters recognition system in the steel production line. Normally, the billets are mixed at yard so that their identifications are very difficult and very important processing. The character recognition algorithm used in this paper is base on the subspace method by K-L transformation. With this method, we need no special feature extraction steps, which are usually error prone. So the gray character images are directly used as input vectors of the classifier. To train the classifier, we have extracted eigen vectors of each character, which was included in the billet images. We have constructed vision system for the recognition of billet characters using this algorithm and tested this system in the steel production line. The recognition rate of our system in the field test has turned out to be 98.6% if the corrupted characters are excluded. In the results, we have confirmed that our recognition system has a good performance in the poor environments and ill-conditioned marking system such as steel production plant. © Springer-Verlag Berlin Heidelberg 2004.