A real-time can-label monitoring system using OMAP-based OCR

An OMAP-based real-time can-label monitoring system has been designed to deal with the recognition problem of cans in a strongly-interfered industrial condition. The dual-core processor OMAP3530 and binocular structure are utilized to guarantee the effective capture and analysis of cans' images. Firstly, the monitoring system captures several images of the cans, then preprocesses these images and outputs the binarized images. Secondly, the binary images are normalized to make sure the images are in the same size. After the image normalization, the monitoring system classifies these images and outputs a number of recognition results by the different features. Lastly, by fusing these results, the system outputs the final recognition result. The experiment results show that the system can resist the high accuracy and robustness as well as overcome the effect of illumination changes and shadows.