Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

Bulatov, Konstantin B.; Fedotova, Nadezhda; Arlazarov, Vladimir V.

doi:10.1109/icpr48806.2021.9412574

Cited by 4 publications

(1 citation statement)

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“…Since its publication, MIDV-500 dataset and its extension MIDV-2019 were used to evaluate the methods of identity document images classification [12 -14]; identity document location [11,15], including the methods based on semantic segmentation [16]; detecting of faces on images of identity documents [17]; and methods related to text fields recognition, including single text line recognition [18], per-frame recognition results combination [19,20] and making a stopping decision in a video stream [21,22]. The dataset was also used to evaluate the methods of choosing a single best frame in the identity document video capture [23] and assessing the quality of the frame for its processing by an identity analysis system [24], detection and masking of sensitive and private information [25] and general ID verification [26].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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