ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

Antonacopoulos, Apostolos; Clausner, Christian; Papadopoulos, Christos; Pletschacher, Stefan

doi:10.1109/icdar.2015.7333941

Cited by 33 publications

(39 citation statements)

References 14 publications

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“…We used three datasets for evaluations: ICDAR2015 [7], SectLabel [36] and our new dataset named DSSE-200. ICDAR2015 [7] is a dataset used in the biennial IC-DAR page segmentation competitions [8] focusing more on appearance-based regions. The evaluation set of IC-DAR2015 consists of 70 sampled pages from contemporary magazines and technical articles.…”

Section: Methodsmentioning

confidence: 99%

Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks

Yang

Yumer

Asente

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

230

163

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We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance.

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Section: Methodsmentioning

confidence: 99%

Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks

Yang

Yumer

Asente

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

230

163

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“…Evaluations of document segmentation algorithms are regularly published either as a dedicated papers [7], [8] or as a competition report [4], [5], [9]- [11]. These papers have investigated how to evaluate a single segmentation result based on a given ground truth.…”

Section: A Evaluation Of Segmentation Algorithmsmentioning

confidence: 99%

Evaluation of the Stability of Four Document Segmentation Algorithms

Eskenazi

Gomez‐Krämer

Ogier

2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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International audienceThe importance of having stable information extraction algorithms for security related applications and more generally for industrial use cases has been recently highlighted. Stability is what makes an algorithm reliable as it gives a guarantee that the results will be reproducible on similar data. Without it, security criteria such as the probability of false positives cannot be quantified. As a consequence, no security application can be built from an unstable algorithm. In a document verification framework, the probability of false positives indicates the probability that two different results are given for two copies of the same document. This paper builds on our previous work about a stable layout descriptor to study the stability of four segmentation algorithms. We consider that a segmentation algorithm is stable if it produces the same layout for all copies of the same document. The algorithms studied are two versions of PAL, Voronoi, and JSEG. We compare the stability of the different algorithms and study the factors influencing their stability

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“…In addition, the used datasets have been selected from curated repositories [4] [5] containing realistic and representative documents. This edition (RDCL2017) is based on the same principles established and refined by the 2011, 2013, and 2015 competitions on historical and contemporary document layout analysis [6] but its focus is on documents with complex layouts. The evaluation scenarios selected for this competition reflect the need to identify robust and accurate methods for large-scale digitisation initiatives.…”

Section: Introductionmentioning

confidence: 99%

ICDAR2019 Competition on Recognition of Documents with Complex Layouts - RDCL2019

Clausner

Antonacopoulos

Pletschacher

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2017, presenting the results of the evaluation of seven methodsfive submitted, two state-of-theart systems (commercial and open-source). Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one focusing only on text regions). For the first time, nested region content (table cells, chart labels etc.) are evaluated in addition to the top-level page content. Text recognition was a bonus challenge and was not taken up by all participants. The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-textual content.

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ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

Cited by 33 publications

References 14 publications

Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks

Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks

Evaluation of the Stability of Four Document Segmentation Algorithms

ICDAR2019 Competition on Recognition of Documents with Complex Layouts - RDCL2019

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