Synthetic dataset of ID and Travel Document
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913184735035392 |
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| author | Boned, Carlos Talarmain, Maxime Ghanmi, Nabil Chiron, Guillaume Biswas, Sanket Awal, Ahmad Montaser Terrades, Oriol Ramos |
| author_facet | Boned, Carlos Talarmain, Maxime Ghanmi, Nabil Chiron, Guillaume Biswas, Sanket Awal, Ahmad Montaser Terrades, Oriol Ramos |
| contents | This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems. Such a dataset has become a necessity as ID documents contain personal information and a public dataset of real documents can not be released. Moreover, forged documents are scarce, compared to legit ones, and the way they are generated varies from one fraudster to another resulting in a class of high intra-variability. In this paper we trained state-of-the-art models on this dataset and we compare them to the performance achieved in larger, but private, datasets. The creation of this dataset will help to document image analysis community to progress in the task of ID document verification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic dataset of ID and Travel Document Boned, Carlos Talarmain, Maxime Ghanmi, Nabil Chiron, Guillaume Biswas, Sanket Awal, Ahmad Montaser Terrades, Oriol Ramos Computer Vision and Pattern Recognition This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems. Such a dataset has become a necessity as ID documents contain personal information and a public dataset of real documents can not be released. Moreover, forged documents are scarce, compared to legit ones, and the way they are generated varies from one fraudster to another resulting in a class of high intra-variability. In this paper we trained state-of-the-art models on this dataset and we compare them to the performance achieved in larger, but private, datasets. The creation of this dataset will help to document image analysis community to progress in the task of ID document verification. |
| title | Synthetic dataset of ID and Travel Document |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.01858 |