Fast Method of ID Documents Location and Type Identification for Mobile and Server Application

Abstract

In this paper we discuss the problem of simultaneous document type recognition and projective distortion parameters estimation for the images of ID documents. There are two considered cases. In the first case a video stream captured using mobile devices is processed on the device. The second case considers photos or scanned images which are processed on a server. For each case the requirements are defined for the input data and processing speed. The universal approach is proposed, which allows solving the problem in both cases. The approach is based on representing the image as a constellation of feature points and descriptors, but in order to perform more accurate distortion parameters estimation straight lines and quadrangles are extracted from the input image and used as additional features. Techniques are described which allow to combine matched feature points, lines, and quadrangles to geometric verification using RANSAC. Best alternative selection criteria are proposed along with methods of solution accuracy estimation. The differences between methods of preliminary analysis of the input image and geometric primitives location are discussed in relation to the considered problems. For quality estimation an open dataset MIDV-500 is used, together with its extension for server-side problem version, created in scope of this work. Results show that using lines and quadrangles increase the location accuracy, and the proposed algorithm surpasses previously published works in classification precision and computational performance.