Title: Pattern classification system for the automatic analysis of paper for recycling

Authors: Anke Gottschling; Samuel Schabel

Addresses: Chair of Paper Technology and Mechanical Process Engineering, Technische Universität Darmstadt, Alexanderstraße 8, 64283 Darmstadt, Germany ' Chair of Paper Technology and Mechanical Process Engineering, Technische Universität Darmstadt, Alexanderstraße 8, 64283 Darmstadt, Germany

Abstract: The objective of this paper is the development of a pattern classification system for the automatic analysis of the composition of samples from paper for recycling. The system uses a colour video camera and dynamic scales as sensors. An overall number of 26 features of the categories weight, shape, colour, texture and amount of optical brighteners are extracted to distinguish between ten classes of paper for recycling. Five classifiers of different types are trained and tested with industrial samples. During testing a piece of paper is assigned to the class most common among the five classifiers, but the classification success rates lie under 50% for some classes and are therefore not acceptable. For this reason, the system is modified to discriminate only between six of the ten classes. For those classes, the classification success rates lie between 94% and 100% which is suitable for the analysis of samples from paper for recycling.

Keywords: paper recycling; recovered paper; sampling; composition; analysis; pattern recognition; pattern classification; image analysis; machine vision; paper collection; paper sorting; colour video camera; dynamic scales; sensors; weight; shape; colour; texture; optical brighteners.

DOI: 10.1504/IJAPR.2016.076986

International Journal of Applied Pattern Recognition, 2016 Vol.3 No.1, pp.38 - 58

Received: 07 Nov 2015
Accepted: 25 Feb 2016
Published online: 16 Jun 2016 *

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