Quality Testing for Pressed Raised Character on Metal Label Using GRBF Networks

摘要

In accordance with the obvious characteristics of the pressed raised character image and the shortages of the template matching method, a new method of using the general radial-basis function neural network (GRBFN) for testing the quality of the pressed character is presented. The structures and training methods of GRBFN are fully analyzed, as well as the functionality of hidden layer, excited focus and area. The results show the checker based on GRBFN has highly checking ratio for the label pressed raised characters. It is suited to the quality testing of raised characters.