Automated sorting of recycled paper using smart image processing

Abstract

Abstract Because of the cost and complexity of implementing an optical paper sorting system, the demand for an intelligent system for waste paper sorting has increased. This research focused on the development of a smart intelligent system (SIS) for recyclable waste paper sorting. The basis for selecting the regions of interests (ROIs) is the margin area of a paper object image because almost all printed documents keep the margin area intact. The paper grade is identified using a proximity search. The SIS with the HSI colour space offered maximum success rates of 99 %, 82 % and 89 %, while with the RGB model, the classification success rates were 94 %, 93 % and 98 % for white paper, old newsprint paper and old corrugated cardboard, respectively. The SIS is clearly superior to other prevailing techniques because of the faster decision making and lower cost of implementation.