Unsupervised Font Reconstruction Based on Token Co-occurrence

Michael P. Cutter, Joost van Beusekom, Faisal Shafait, Thomas Breuel

In: ACM Symposium on Document Engineering. ACM Symposium on Document Engineering (DocEng-10) September 21-24 Manchester United Kingdom ACM 9/2010.


High quality conversions of scanned documents into PDF usually either rely on full OCR or token compression. This paper describes an approach intermediate between those two: it is based on token clustering, but additionally groups tokens into candidate fonts. Our approach has the potential of yielding OCR-like PDFs when the inputs are high quality and degrading to token based compression when the font analysis fails, while preserving full visual fidelity. Our approach is based on an unsupervised algorithm for grouping tokens into candidate fonts. The algorithm constructs a graph based on token proximity and derives token groups by partitioning this graph. In initial experiments on scanned 300 dpi pages containing multiple fonts, this technique reconstructs candidate fonts with 100% accuracy.


Cutter-Font-Reconstruction-DocEng10.pdf (pdf, 193 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz