2019/10/09 by Konstantin Bulatov, Daniil Matalov, Vladimir V. Arlazarov · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #CLIPS #Face recognition and analysis #Facial recognition system #Field (mathematics) #Handwritten Text Recognition Techniques #Identification (biology) #Identity (music) #Key (lock) #Mobile device #Optical character recognition #cs.CV #msc:68T45
paper · pdf · doi:10.1117/12.2558438
published as Proc. SPIE 11433 ICMV-2019 (2020), 114332N · 6 pages, 3 figures, 3 tables, 18 references, submitted and accepted to the 12th International Conference on Machine Vision (ICMV 2019)
arxiv created 2019/10/09 · openalex created_date 2019/10/18 · openalex publication_date 2020/01/31 · arxiv updated 2020/02/12 · openalex updated_date 2026/08/05
Recognition of identity documents using mobile devices has become a topic of a wide range of computer vision research. The portfolio of methods and algorithms for solving such tasks as face detection, document detection and rectification, text field recognition, and other, is growing, and the scarcity of datasets has become an important issue. One of the openly accessible datasets for evaluating such methods is MIDV-500, containing video clips of 50 identity document types in various conditions. However, the variability of capturing conditions in MIDV-500 did not address some of the key issues, mainly significant projective distortions and different lighting conditions. In this paper we present a MIDV-2019 dataset, containing video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. The description of the added data is presented, and experimental baselines for text field recognition in different conditions.