Publication | Open Access
cBAD: ICDAR2017 Competition on Baseline Detection
78
Citations
8
References
2017
Year
Unknown Venue
Scene AnalysisEngineeringMachine LearningBiometricsImage Sequence AnalysisImage AnalysisData SciencePattern RecognitionText RecognitionCbad CompetitionCharacter RecognitionIcdar2017 CompetitionIcdar 2013Machine VisionBenchmark DatasetsOptical Character RecognitionComputer ScienceDeep LearningComputer VisionHandwriting Segmentation ContestScene UnderstandingDocument Processing
The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms. It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation Contest. A new, challenging, dataset was created to test the behavior of state-of-the-art systems on real world data. Since traditional evaluation schemes are not applicable to the size and modality of this dataset, we present a new one that introduces baselines to measure performance. We received submissions from five different teams for both tracks.
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