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Impact of geometrical restrictions in RANSAC sampling on the ID document classification
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2020
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Geometrical RestrictionsEngineeringMachine LearningBiometricsText MiningRansac SchemeClassification MethodImage AnalysisInformation RetrievalData ScienceData MiningPattern RecognitionText RecognitionDocument ClassificationComputational GeometryStatisticsMachine VisionAutomatic ClassificationOptical Character RecognitionKnowledge DiscoveryComputer ScienceComputer VisionSpatial VerificationData ClassificationId Document ClassificationDistortion ParametersDocument Processing
In this paper we explore the impact of geometrical restrictions in RANSAC sampling on the ID document type recognition accuracy in images, as well as on the accuracy of the projective distortion parameters estimation. The studied method is based on representing images as constellations of keypoints and their descriptors. The distortion parameters are estimated by applying RANSAC on the matched keypoints. Cases are studied where the base algorithm can yield erroneous or insufficiently accurate solution. A RANSAC scheme is presented with geometrical restrictors and several restriction are proposed, limiting the samples and the computed transform parameters. An experiment was conducted on the open dataset MIDV-500 and the data is presented of the dependence of classification and localization accuracy on the considered restrictors. It was shown that the introduction of restrictors allows to achieve a accuracy improvement and significant speed up.