Fast Method of ID Documents Location and Type Identification for Mobile and Server Application

Skoryukina, Natalya; Arlazarov, Vladimir V.; Nikolaev, Dmitry P.

doi:10.1109/icdar.2019.00141

Cited by 28 publications

(34 citation statements)

References 16 publications

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“…The localisation accuracy, using a threshold for IoU of 0.9, was 92.8% for the private dataset and 97% for MIDV500. In [40], a method for simultaneous location and document type recognition is performed on ID document images. There are two considered cases, video in mobile devices, photos and scanned images on a server.…”

Section: Related Workmentioning

confidence: 99%

Towards an Efficient Semantic Segmentation Method of ID Cards for Verification Systems

Lara¹,

Valenzuela²,

Schulz³

et al. 2021

Preprint

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Removing the background in ID Card images is a real challenge for remote verification systems because many of the re-digitalised images present cluttered backgrounds, poor illumination conditions, distortion and occlusions. The background in ID Card images confuses the classifiers and the text extraction. Due to the lack of available images for research, this field represents an open problem in computer vision today. This work proposes a method for removing the background using semantic segmentation of ID Cards. In the end, images captured in the wild from real operation, using a manually labelled dataset consisting of 45,007 images, with five types of ID Cards from three countries (Chile, Argentina and Mexico), including typical presentation attack scenarios, were used. This method can help to improve the following stages in a regular identity verification or document tampering detection system. Two Deep Learning approaches were explored, based on MobileUNet and DenseNet10. The best results were obtained using MobileUNet, with 6.5 million parameters. A Chilean ID Card's mean Intersection Over Union (IoU) was 0.9926 on a private test dataset of 4,988 images. The best results for the fused multi-country dataset of ID Card images from Chile, Argentina and Mexico reached an IoU of 0.9911. The proposed methods are lightweight enough to be used in real-time operation on mobile devices.

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Section: Related Workmentioning

confidence: 99%

Towards an Efficient Semantic Segmentation Method of ID Cards for Verification Systems

Lara¹,

Valenzuela²,

Schulz³

et al. 2021

Preprint

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show abstract

“…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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“…Skoryukina et al 88 studied the simultaneous ID document and their projective distortion parameters. They considered two possible instances.…”

Section: Id Document Classificationmentioning

confidence: 99%

An Intelligent Hybrid Model for Identity Document Classification

Khandan

2021

Preprint

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Digitization, i.e., the process of converting information into a digital format, may provide various opportunities (e.g., increase in productivity, disaster recovery, and environmentally friendly solutions) and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. For example, processes in banking (e.g., applying for loans) or the Government Registry of BDM (Births, Deaths, and Marriages) applications may involve uploading several documents such as a driver's license and passport. There are not many studies available to address the challenge as an application of image classification. Although some studies are available which used various methods, a more accurate model is still required. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance of the proposed model is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.

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Fast Method of ID Documents Location and Type Identification for Mobile and Server Application

Cited by 28 publications

References 16 publications

Towards an Efficient Semantic Segmentation Method of ID Cards for Verification Systems

Towards an Efficient Semantic Segmentation Method of ID Cards for Verification Systems

ID documents matching and localization with multi-hypothesis constraints

An Intelligent Hybrid Model for Identity Document Classification

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