Identity Documents Classification as an Image Classification Problem

Sicre, Ronan; Awal, Ahmad Montaser; Furon, Teddy

doi:10.1007/978-3-319-68548-9_55

Cited by 16 publications

(18 citation statements)

References 29 publications

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“…Therefore, they might not be the first choice for building a generic document spotting and localization solution. Moreover, end-to-end deep learning approaches require a large number of documents to be trained [18], which again is the main disadvantage in our context since we deal with identity documents hardly collectible in mass. Even if transferable pre-trained models could lower the need for training samples, it would not help much for validation and tests.…”

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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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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“…The processing of images of identification documents has received much attention in the literature. Researchers have presented approaches for identification documents classification [4], automatic handwritten signature segmentation [5], document boundary detection and document text detection [3]. As shown in Figure 1, the proposed algorithm is divided into six main steps, which are detailed below:…”

Section: Related Workmentioning

confidence: 99%

“…Once the organizations have the images of identification documents of their customers, they can execute some algorithms for the automation of the text field extraction tasks [3], document classification [4], signature extraction [5], in addition to other properties and patterns present in the identification documents images.…”

Section: Introductionmentioning

confidence: 99%

“…Personal identification documents contain confidential information that cannot be made public; consequently, there are no datasets of images of identification documents freely available on the internet. In this context, image processing research teams that would like to apply their algorithms to identification documents, need industrial partners who provide the dataset [4], or invest much time and effort to build their own datasets [3] [9]. In particular, due to the Brazilian personal data privacy law [10], there are several restrictions to access, store, and share individual documents.…”

Section: Introductionmentioning

confidence: 99%

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BID Dataset: a challenge dataset for document processing tasks

Soares.¹,

Neves²,

Bezerra³

2020

Anais Estendidos Da Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2020)

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The digital relationship between companies and customers happens through online systems where consumers must upload their identification documents pictures to prove their identities. The existence of this large volume of document images encourages the research development to generate image processing systems to automate tasks usually performed by humans, such as Document Type Classification and Document Reading. The lack of identification documents public datasets delays the research development in document image processing because researchers need to attempt partnerships with private or governmental institutions to obtain the data or build their dataset. In this context, this work presents as main contributions a system to support the automatic creation of identification document public datasets and the Brazilian Identity Document Dataset (BID Dataset): the first Brazilian identification documents public dataset. To accomplish the current personal data privacy law, all information in the BID Dataset comes from fake data. This work aims to increase the velocity of research development in identification document image processing, considering that researchers will be able to use the BID Dataset to develop their research freely.

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“…The work of [27] performs an extensive evaluation on the image datasets using state of the art image classification methods. Our system is compared to CNN-based classification using the 'fast' network [5] on both FRA DB and BEL DB.…”

Section: Experimentationmentioning

confidence: 99%

Complex Document Classification and Localization Application on Identity Document Images

Awal¹,

Ghanmi²,

Sicre³

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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This paper studies the problem of document image classification. More specifically, we address the classification of documents composed of few textual information and complex background (such as identity documents). Unlike most existing systems, the proposed approach simultaneously locates the document and recognizes its class. The latter is defined by the document nature (passport, ID, etc.), emission country, version, and the visible side (main or back). This task is very challenging due to unconstrained capturing conditions, sparse textual information, and varying components that are irrelevant to the classification, e.g. photo, names, address, etc.First, a base of document models is created from reference images. We show that training images are not necessary and only one reference image is enough to create a document model. Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. The matching process is optimized to guarantee an execution time independent from the number of document models. Once the document model is found, a more accurate matching is performed to locate the document and facilitate information extraction. Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%.

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Identity Documents Classification as an Image Classification Problem

Cited by 16 publications

References 29 publications

ID documents matching and localization with multi-hypothesis constraints

ID documents matching and localization with multi-hypothesis constraints

BID Dataset: a challenge dataset for document processing tasks

Complex Document Classification and Localization Application on Identity Document Images

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