Weakly Supervised Learning-based Table Detection

Gurav, A. A.; Nene, Manisha J.

doi:10.1007/s42979-020-0113-x

“…Last but not the least, there are some other weakly supervised approaches [4,20,48] to perform structural analysis of specific objects like Music Scores, Table , Handwritten pages, and so on. However, none of the above techniques perform instance segmentation of a document containing multiple categories in a domain invariant way which motivates us to solve this task.…”

Section: Weakly Supervised Dlamentioning

confidence: 99%

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

Banerjee,

Biswas,

Lladós

et al. 2024

IJDAR

1

0

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Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach.

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“…Last but not the least, there are some other weakly supervised approaches [4,20,48] to perform structural analysis of specific objects like Music Scores, Table , Handwritten pages, and so on. However, none of the above techniques perform instance segmentation of a document containing multiple categories in a domain invariant way which motivates us to solve this task.…”

Section: Weakly Supervised Dlamentioning

confidence: 99%

SemiDocSeg: Harnessing Semi-Supervised Learning for Document Layout Analysis

Banerjee,

Biswas,

Lladós

et al. 2023

Preprint

1

0

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Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach.

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Deep Learning for Table Detection and Structure Recognition: A Survey

Salaheldin Kasem,

Abdallah,

Berendeyev

et al. 2024

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Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition.

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Weakly Supervised Learning-based Table Detection

Cited by 3 publications

References 17 publications

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

SemiDocSeg: Harnessing Semi-Supervised Learning for Document Layout Analysis

Deep Learning for Table Detection and Structure Recognition: A Survey

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