A protocol for performance evaluation of line detection algorithms
Machine Vision and Applications - Special issue on performance evaluation
Incremental Arc Segmentation Algorithm and Its Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended Summary of the Arc Segmentation Contest
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Arabic Handwriting Recognition Competition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
The third report of the arc segmentation contest
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
GREC'09 arc segmentation contest: performance evaluation on old documents
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Empirical performance evaluation of raster to vector conversion with different scanning resolutions
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
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Automatic conversion of line drawings from paper to electronic form requires the recognition of geometric primitives like lines, arcs, circles etc. in scanned documents. Many algorithms have been proposed over the years to extract lines and arcs from document images. To compare different state-of-the-art systems, an arc segmentation contest was held in the seventh IAPR International Workshop on Graphics Recognition - GREC 2007. Four methods participated in the contest, three of which were commercial systems and one was a research algorithm. This paper presents the results of the contest by giving an overview of the dataset used in the contest, evaluation methodology, participating methods and the segmentation accuracy achieved by the participating methods.