Robust and Precise Circular Arc Detection

The original publication is available at www.springerlink.comInternational audienceIn this paper we present a method to robustly detect circular arcs in a line drawing image. The method is fast, robust and very reliable, and is capable of assessing the quality of its detection. It is based on Random Sample Consensus minimization, and uses techniques that are inspired from object tracking in image sequences. It is based on simple initial guesses, either based on connected line segments, or on elementary mainstream arc detection algorithms. Our method consists of gradually deforming these circular arc candidates as to precisely fit onto the image strokes, or to reject them if the fitting is not possible, this virtually eliminates spurious detections on the one hand, and avoiding non-detections on the other hand

Document Analysis Research in the Year 2021

The original publication is available at www.springerlink.comInternational audienceDespite tremendous advances in computer software and hardware, certain key aspects of experimental research in document analysis, and pattern recognition in general, have not changed much over the past 50 years. This paper describes a vision of the future where community-created and managed resources make possible fundamental changes in the way science is conducted in such fields. We also discuss current developments that are helping to lead us in this direction