Chromaticity based waste paper grade identification

In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. As a consequence, many automated mechanical and optical paper sorting methods have been developed to fill the paper sorting demand during 1932 to 2009. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The implementation of the previous methods, while being a step forward in the large-volume automated sorting technology, is still complex, expensive and sometimes offers limited reliability. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using chromaticity. For constructing template database, hue and saturation of the paper object image in a selected area are considered. The paper grade is identified based on the maximum occurrence of a specific template in the paper object image. The classification success rates for white paper, old newsprint paper and old corrugated cardboard are 95%, 92% and 90%, respectively. Finally, the best result of the proposed method is compared with the results published in literature where waste paper grade identification systems were developed using other methods. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using chromaticity, which is the best among the prevailing techniques of optical or electronic image based systems.