ICDAR2017 Competition on Recognition of Documents with Complex Layouts - RDCL2017

Clausner, Christian; Antonacopoulos, Apostolos; Pletschacher, Stefan

doi:10.1109/icdar.2017.229

Cited by 48 publications

(37 citation statements)

References 18 publications

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“…In addition, there is an ethical concern regarding verifiable and reproducible research, especially during peer review process. Even great papers presenting state-of-the-art results can be rejected by the [1,9] Document image quality assessment [19,21] General image understanding [11,18] scientific community for being impossible to verify without the testing data. In this paper we present a Mobile Identity Document Video dataset (MIDV-500), which in contrast to other relevant publicly available datasets can be used to develop, demonstrate and benchmark a coherent processing pipeline of identity document analysis and recognition in its modern applications and use cases.…”

Section: Introductionmentioning

confidence: 99%

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

Arlazarov

Bulatov

Chernov³

2019

Computer Optics

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A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity documents recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide This work is partially supported by Russian Foundation for Basic Research (projects 17-29-03170 and 17-29-03370). Source images for MIDV-500 datasets are obtained from Wikimedia Commons

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Section: Introductionmentioning

confidence: 99%

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

Arlazarov

Bulatov

Chernov³

2019

Computer Optics

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“…For the first database, we have extracted 690 region images from 70 pages of RDCL 2015 (Antonacopoulos et al 2015) and 75 pages of RDCL 2017 (Clausner et al 2017) databases. These document pages are generated in the PRIMA research lab, University of Salford, UK.…”

Section: Preparation Of Databasesmentioning

confidence: 99%

Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm

et al. 2021

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Text non-text separation is one of the most essential pre-processing steps for any optical character recognition (OCR) system. As an OCR engine can only process texts, the non-texts present in an input document image are required to be suppressed at the initial level. Therefore, to build a complete OCR system, an efficient text non-text separation module needs to be developed. To this end, we have proposed a texture-based feature descriptor followed by a novel feature selection technique for region-based text non-text classification. First, we have incorporated rotation invariant property with local ternary pattern to form a new texture-based feature descriptor, rotation invariant local ternary pattern (RILTP). Next, a novel feature selection technique is proposed which is a modified version of binary particle swarm optimization (BPSO). For the evaluation of the proposed text non-text classification method, we have initially constructed a database consisting of 690 images of text and non-text regions extracted from 70 pages of RDCL 2015 and 75 pages of RDCL 2017 page segmentation competitions databases. In this database, each class contains 345 data samples. The proposed texturebased feature descriptor has obtained an accuracy of 97.09% on this database. Whereas, after applying BPSO, the feature dimension is reduced by approximately 55% and at the same time, the accuracy reaches 97.5%. Furthermore, in this work, another database is also created from Media team document pages to validate the robustness of this method. The second database comprises 100 text and 100 non-text images. The method has achieved 96.28% accuracy when it is trained with the first database and tested with the second database. The comparative study reveals the robustness and strength of the proposed method as it outnumbers many state-of-the-art texture-based features. Besides, the proposed feature selection method is also compared with various standard feature selection methods, and it has been observed that the proposed one outperforms all those methods considered here for comparison.

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“…The recognition of page objects respectively the segmentation of a document in its logical components was also the topic of recent competitions, in the following just naming a few. At the ICDAR2015/2017/2019 Competition on Recognition of Documents with Complex Layouts [3,7,8], the participants had to segment scanned pages from contemporary magazines and technical articles, classify the resulting regions and to apply text recognition as a bonus challenge.…”

Section: Related Workmentioning

confidence: 99%

“…https://github.com/CITlabRostock/citlab-python-util/tree/master/citlab python util/parser/xml/page 7. https://github.com/CITlabRostock/citlab-article-separation-measure.…”

mentioning

confidence: 99%

ICPR 2020 Competition on Text Block Segmentation on a NewsEye Dataset

Michael

Weidemann

Laasch

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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We present a competition on text block segmentation within the framework of the International Conference on Pattern Recognition (ICPR) 2020. The main goal of this competition is to automatically analyse the structure of historical newspaper pages with a subsequent evaluation of the participants' algorithms performance. In contrast to many existing segmentation methods, instead of working on pixels, the present study has a focus on clustering baselines/text lines into text blocks. Therefore, we introduce a new measure based on a baseline detection evaluation scheme. But also common pixel-based approaches could participate without restrictions. Working on baseline level addresses directly the application scenario where for a given image the contained text should be extracted in blocks for further investigations. We present the results of three submissions. The experiments have shown that text blocks can be reliably detected both on pages with a simple layout and on pages with a complex layout.

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ICDAR2017 Competition on Recognition of Documents with Complex Layouts - RDCL2017

Cited by 48 publications

References 18 publications

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm

ICPR 2020 Competition on Text Block Segmentation on a NewsEye Dataset

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