9 October 1998 Long-range optical character recognition for product ID
Author Affiliations +
Proceedings Volume 3517, Intelligent Systems in Design and Manufacturing; (1998); doi: 10.1117/12.326935
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States
An automated product tag reading system based on CCD cameras and computer image processing has been developed by West Virginia University and demonstrated at the Weirton Steel Corporation. The system reads both a 50-mil barcode and a string of six numbers on a four-inch by six-inch tag fastened to the end of a steel slab. Feedback from the image is used to point and zoom the camera, making the system effective at ranges up to thirty feet, and in bright sunlight-situations where handheld barcode scanners are ineffective. A video camera is mounted on a pan/tilt head and connected to a personal computer through a frame grabber board. The whole system is mounted on a slab hauler--a huge wheeled machine for carrying 100 tons of steel slabs at a time. The slab hauler backs into position and presses `start' on a touchscreen operator interface. A wide-angle image is grabbed, and the computer analyzes the images to find product ID tags in scene. The camera is then zoomed and pointed one-by-one at the tags for closeup images. Geometric warping is done on the closeup images to correct for viewing angle distortion, and both the barcode and the alphanumeric code are read by the software and reported to the inventory management system via radio modern. This paper discusses the neural network-based system for reading the characters on the tag. The camera pointing system and barcode reader are discussed in a companion paper.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Larry E. Banta, Franz A. Pertl, Kimberly A. Rosenberry-Friend, "Long-range optical character recognition for product ID", Proc. SPIE 3517, Intelligent Systems in Design and Manufacturing, (9 October 1998); doi: 10.1117/12.326935; https://doi.org/10.1117/12.326935


Image segmentation


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Neural networks

Imaging systems

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