Impact of geometrical restrictions in RANSAC sampling on the ID document classification

Skoryukina, Natalya; Faradjev, Igor; Bulatov, Konstantin B.; Arlazarov, Vladimir V.

doi:10.1117/12.2559306

Cited by 10 publications

(4 citation statements)

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“…Within the scope of the benchmark evaluation, in order to maximize reproducibility, the search for direct and inverse matching was performed without any randomized approximation, and no additional geometric restrictions were imposed, following the procedure described in [13]. For keypoints detection the SURF method was employed [45], and for matching the keypoints we evaluated the SURF descriptor [45], and the binary descriptor BEBLID [46] in 256-and 512-dimension variants.…”

Section: Examples Of Semantic Segmentation Results Of Images From Mid...mentioning

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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Section: Examples Of Semantic Segmentation Results Of Images From Mid...mentioning

confidence: 99%

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

et al. 2022

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“…In the work of Ranftl and Koltun [44] outliers are removed via geometric model estimation and the underlying fundamental matrix is computed using deep neural networks. More recently, Skoryukina et al [51] proposed a RANSAC scheme with geometrical restrictors, focusing on ID document classification. For this case of planar object matching, improvements in accuracy are achieved.…”

Section: Related Workmentioning

confidence: 99%

Dense Feature Matching Based on Homographic Decomposition

2022

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Finding robust and accurate feature matches is a fundamental problem in computer vision. However, incorrect correspondences and suboptimal matching accuracies lead to significant challenges for many real-world applications. In conventional feature matching, corresponding features in an image pair are greedily searched using their descriptor distance. The resulting matching set is then typically used as input for geometric model fitting methods to find an appropriate fundamental matrix and filter out incorrect matches. Unfortunately, this basic approach cannot solve all practical problems, such as fundamental matrix degeneration, matching ambiguities caused by repeated patterns and rejection of initially mismatched features without further reconsideration. In this paper we introduce a novel matching pipeline, which addresses all of the aforementioned challenges at once: First, we perform iterative rematching to give mismatched feature points a further chance for being considered in later processing steps. Thereby, we are searching for inliers that exhibit the same homographic transformation per iteration. The resulting homographic decomposition is used for refining matches, occlusion detection (e.g. due to parallaxes) and extrapolation of additional features in critical image areas. Furthermore, Delaunay triangulation of the matching set is utilized to minimize the repeated pattern problem and to implement focused matching. Doing so, enables us to further increase matching quality by concentrating on local image areas, defined by the triangular mesh. We present and discuss experimental results with multiple real-world matching datasets. Our contributions, besides improving matching recall and precision for image processing applications in general, also relate to use cases in image-based computer graphics.

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“…All-in-one classification and localization solutions are proposed in [4], [19], and [20]. In this context, supported document models are generated using a unique reference image captured in good conditions without any distortion.…”

Section: A Document Localization Approachesmentioning

confidence: 99%

“…A similar approach is followed by [19] and [20] but with additional features considered for the estimation of the transformation matrix H. While [19] simply checks on straight lines and quadrilateral topology, [20] revisits RANSAC to incorporate constraints on homography convexity and specific spatial dispersion of matched keypoints.…”

Section: A Document Localization Approachesmentioning

confidence: 99%

ID documents matching and localization with multi-hypothesis constraints

Chiron

Ghanmi

Awal

2021

2020 25th International Conference on Pattern Recognition (ICPR)

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This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, nonrigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.Best selected hypothesis repatriation in docs % (-are ablated hypothesis)Accepted crops >0.9 Jaccard dist Keypoints 3D trans.

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Impact of geometrical restrictions in RANSAC sampling on the ID document classification

Cited by 10 publications

References 0 publications

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

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

Dense Feature Matching Based on Homographic Decomposition

ID documents matching and localization with multi-hypothesis constraints

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