MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis

Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of mock identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In addition, the published datasets were typically designed only for a subset of document recognition problems, not for a complex identity document analysis. In this paper, we present a dataset MIDV-2020 which consists of 1000 annotated video clips, 1000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces. For the presented benchmark dataset baselines are provided for such tasks as document detection, text fields recognition and end-to-end identity document recognition. To date, the proposed dataset is the largest publicly available identity documents dataset with variable artificially generated data, and we believe that it will prove invaluable for advancement of the field of document analysis and recognition. 


The set of base document types for MIDV-2020 comprises 10 document types, each present in previously published MIDV-500 and MIDV-2019 datasets. The identity document types of MIDV-2020 are listed in Table DOCTYPES. 100 sample documents were created for each of the 10 document types present in the datasets. More detailed information can be found here: https://arxiv.org/ftp/arxiv/papers/2107/2107.00396.pdf






Clips Scan_upright Scan_rotate Photo Photo

Any use of this dataset is required to cite the following reference:
K.B. Bulatov, E.V. Emelianova, D.V. Tropin, N.S. Skoryukina, Y.S. Chernyshova, A.V. Sheshkus, S.A. Usilin, Z. Ming, J.-C. Burie, M. M. Luqman, V.V. Arlazarov: “MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis”, Computer Optics (submitted), 2021.

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The dataset has a size of 124GB and is hosted on an sFTP server of the University of La Rochelle (France). Please fill the following form for getting access to the dataset. You need to accept the licence of the dataset to have access to it.