MIDV-2020: a comprehensive benchmark dataset for identity document analysis

Abstract

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 identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. The dataset contains 72409 annotated images in total, making it the largest publicly available identity document dataset to the date of publication. We describe the structure of the dataset, its content and annotations, and present baseline experimental results to serve as a basis for future research. For the task of document location and identification content-independent, feature-based, and semantic segmentation-based methods were evaluated. For the task of document text field recognition, the Tesseract system was evaluated on field and character levels with grouping by field alphabets and document types. For the task of face detection, the performance of Multi Task Cascaded Convolutional Neural Networks-based method was evaluated separately for different types of image input modes. The baseline evaluations show that the existing methods of identity document analysis have a lot of room for improvement given modern challenges. We believe that the proposed dataset will prove invaluable for advancement of the field of document analysis and recognition.

Document Liveness Challenge Dataset (DLC-2021)

“…The GDPR [ 7 ] and other local laws prohibit the creation of datasets with real ID images. Thus, researchers began to use artificially generated ID document images for open dataset creation [ 8 , 9 , 10 , 11 , 12 ]. As far as we know, printed mock documents are used only in MIDV family datasets, and MIDV-500 was the first [ 8 ].…”

“…The dataset was also supplemented with photos and scanned images of the same document types to represent the typical input for server-side identity document analysis systems. MIDV-2020 [ 10 ] was published recently to provide variability in the text fields, faces, and signatures, while retaining the realism of the dataset. The MIDV-2020 dataset consists of 1000 different physical documents (100 documents per type), all with unique, artificially generated faces, signatures, and text field data.…”

“…Thus, all images in the MIDV family of datasets [ 8 , 9 , 10 , 11 ] can be considered as images of genuine documents and can be used as negative samples for document fraud detectors.…”

“…Frames were extracted at 10 frames per second using ffmpeg version n4.4 with default parameters, and for the first 50 extracted frames the document position was manually annotated. The annotation file for each clip followed the MIDV-2020 JSON format [ 10 ] and was readable with VGG Image Annotator (v2.0.11) [ 29 ].…”

Memory Efficient Local Features Descriptor for Identity Document Detection on Mobile and Embedded Devices

“…The associate editor coordinating the review of this manuscript and approving it for publication was Jiju Poovvancheri . location and classification [2], [3]. A high-quality solution to these problems plays a significant role in the document recognition process.…”