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 26 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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“…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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“…Templates define the strictest constraints on the location of each zone and are most commonly used for identity documents. In [25,29,30], document templates are used for the localization and classification of document images, but they are also helpful in field segmentation [31]. Flexible form models are based on text segmentation and recognition result analysis and describe documents with soft restrictions on their structure.…”

Section: Document Image Quality Assessment Problem Statementmentioning

confidence: 99%

A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

Shemiakina¹,

Limonova

Skoryukina³

et al. 2021

Mathematics

Self Cite

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In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.

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

Cited by 26 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

A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

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