Low quality string recognition for factory automation

Abstract

Describes a method for dot-printed character string recognition on a piece of steel for factory automation. Our scanned string consists of alphanumerics and the '-' character, and the number of characters is variable from 6 to 12 characters. We propose a new recognition procedure for low-quality strings. The procedure includes image emphasis with a Gaussian Laplacian filter, the extraction of the string subimage, segmentation-recognition with dynamic programming, and fine character recognition. We evaluated its accuracy on a UNIX workstation for 1036 images (8806 characters) scanned by a monochrome video camera in the actual production line at a steel-producing factory, and the average recognition rates were 99.2% for the character recognition and 91.6% for the string recognition.