Impact of Geometrical Restrictions in RANSAC Sampling on the ID Document Classification

摘要

In this paper we explore the impact of geometrical restrictions in RANSAC sampling on the ID document typerecognition accuracy in images, as well as on the accuracy of the projective distortion parameters estimation. The studiedmethod is based on representing images as constellations of keypoints and their descriptors. The distortion parameters areestimated by applying RANSAC on the matched keypoints. Cases are studied where the base algorithm can yielderroneous or insufficiently accurate solution. A RANSAC scheme is presented with geometrical restrictors and severalrestriction are proposed, limiting the samples and the computed transform parameters. An experiment was conducted onthe open dataset MIDV-500 and the data is presented of the dependence of classification and localization accuracy on theconsidered restrictors. It was shown that the introduction of restrictors allows to achieve a accuracy improvement andsignificant speed up.