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
TIF2VEC, An Algorithm for Arc Segmentation in Engineering Drawings
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Generating Ground Truthed Dataset of Chart Images: Automatic or Semi-automatic?
Graphics Recognition. Recent Advances and New Opportunities
Building Synthetic Graphical Documents for Performance Evaluation
Graphics Recognition. Recent Advances and New Opportunities
Adaptive noise reduction for engineering drawings based on primitives and noise assessment
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Generation of learning samples for historical handwriting recognition using image degradation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
An efficient parametrization of character degradation model for semi-synthetic image generation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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Line detection algorithms constitute the basis fortechnical document analysis and recognition. Theperformance of these algorithms decreases as the qualityof the documents degrades. To test the robustness of linedetection algorithms under noisy circumstance, wepropose a document degradation mode, which simulatesnoise types that drawings may undergo during theirproduction, storage, photocopying, or scanning. Using ourmodel, a series of document images at various noise levelsand types can be generated for testing the performance ofline detection algorithms. To illustrate that our model isconsistent with real world noise types, we validated themethod by applying it to three line recognition algorithms.