A New Image Dataset for Document Corner Localization

Dizaj, Shima Baniadam; Soheili, Mohammad Reza; Mansouri, Azadeh

doi:10.1109/mvip49855.2020.9116896

Cited by 3 publications

(15 citation statements)

References 6 publications

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“…We run the version on a take a look at dataset and examine our effects to the formerly launched effects at the equal dataset [6]. While this test is enough to assess the device's accuracy at the opposition dataset, it does have drawbacks.…”

Section: B Resultsmentioning

confidence: 99%

“…The end result is as compared with all famous methods, algorithms, and cellular programs that could resolve the report localization assignment in snap shots. The different methods' effects are as compared primarily based totally on [6] (Fig. 3).…”

Section: B Resultsmentioning

confidence: 99%

“…Table 1. The result of the proposed method on dataset [6] Simple Moderate Difficult Average 100% 84% 65% 83%…”

Section: B Resultsmentioning

confidence: 99%

“…In the paper [6] a new document dataset is presented that is closer to the real-world images taken by users. The data separated and labeled into simple, middle, and complex tasks for detection.…”

Section: A Existed Datasetsmentioning

confidence: 99%

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Document Localization in Images Taken by Smartphones Using a Fully Convolutional Neural Network

Dizaj¹,

Soheili²,

Mansouri³

2021

Preprint

Self Cite

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Today integration of facts from virtual and paper files may be very vital for the expertise control of efficient. This calls for the record to be localized at the photograph. Several strategies had been proposed to resolve this trouble; however, they may be primarily based totally on conventional photograph processing strategies that aren't sturdy to intense viewpoints and backgrounds. Deep Convolutional Neural Networks (CNNs), on the opposite hand, have demonstrated to be extraordinarily sturdy to versions in history and viewing attitude for item detection and classification responsibilities. We endorse new utilization of Neural Networks (NNs) for the localization trouble as a localization trouble. The proposed technique ought to even localize photos that don't have a very square shape. Also, we used a newly accrued dataset that has extra tough responsibilities internal and is in the direction of a slipshod user. The end result knowledgeable in 3 exclusive classes of photos and our proposed technique has 83% on average. The end result is as compared with the maximum famous record localization strategies and cell applications.

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Section: B Resultsmentioning

confidence: 99%

Section: B Resultsmentioning

confidence: 99%

“…Table 1. The result of the proposed method on dataset [6] Simple Moderate Difficult Average 100% 84% 65% 83%…”

Section: B Resultsmentioning

confidence: 99%

Section: A Existed Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

Document Localization in Images Taken by Smartphones Using a Fully Convolutional Neural Network

Dizaj¹,

Soheili²,

Mansouri³

2021

Preprint

Self Cite

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show abstract

“…Dizaj, Shima et al [14] presented in 2020 a dataset for document corners localization. The dataset contains 1111 images captured in various conditions from smartphones.…”

Section: Related Workmentioning

confidence: 99%

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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Localization Using DeepLab in Document Images Taken by Smartphones

Dizaj

2022

Lecture Notes in Networks and Systems

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The seamless integration of statistics from virtual and paper files could be very crucial for the know-how control of efficient. A handy manner to obtain that is to digitize a report from a picture. This calls for the localization of the report in the picture. Several approaches are deliberate to resolve this hassle; however, they are supported historical picture method strategies that are not robust to intense viewpoints and backgrounds. Deep Convolutional Neural Networks (CNNs), on the opposite hand, have been validated to be extraordinarily strong to versions in heritage and perspective of view for item detection and classification duties. Inspired by their robustness and generality, we advocate a CNN-primarily based totally technique for the correct localization of files in real-time. We advocate the new utilization of Neural Networks (NNs) for the localization hassle as a key factor detection hassle. The proposed technique ought to even localize snapshots that don't have a very square shape. Also, we used a newly amassed dataset that has extra tough duties internal and is in the direction of a slipshod user. The result is knowledgeable in 3 specific classes of snapshots and our proposed technique has 100% accuracy on easy one and 77% on average. The result is as compared with the maximum famous report localization strategies and cell applications.

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A New Image Dataset for Document Corner Localization

Cited by 3 publications

References 6 publications

Document Localization in Images Taken by Smartphones Using a Fully Convolutional Neural Network

Document Localization in Images Taken by Smartphones Using a Fully Convolutional Neural Network

BID Dataset: a challenge dataset for document processing tasks

Localization Using DeepLab in Document Images Taken by Smartphones

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