An Expert System for Quality Assurance of Document Image Collections

Digital preservation workflows for automatic acquisition of image collections are susceptible to errors and require quality assurance. This paper presents an expert system that supports decision making for page duplicate detection in document image collections. Our goal is to create a reliable inference engine and a solid knowledge base from the output of an image processing tool that detects duplicates based on methods of computer vision. We employ artificial intelligence technologies (i.e. knowledge base, expert rules) to emulate reasoning about the knowledge base similar to a human expert. A statistical analysis of the automatically extracted information from the image comparison tool and the qualitative analysis of the aggregated knowledge are presented.