ClusTi: Clustering Method for Table Structure Recognition in Scanned Images

Zucker, Arthur; Belkada, Younes; Vu, Hanh; Nguyen, Van Nam

doi:10.1007/s11036-021-01759-9

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…A Zucker [107] presents CluSTi, a Clustering approach for recognizing the Structure of Tables in invoice scanned images, as an effective way. CluSTi makes three contributions.…”

Section: Table Structure Recognition Modelsmentioning

confidence: 99%

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Deep learning for table detection and structure recognition: A survey

Kasem¹,

Abdallah²,

Berendeyev³

et al. 2022

Preprint

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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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“…A Zucker [107] presents CluSTi, a Clustering approach for recognizing the Structure of Tables in invoice scanned images, as an effective way. CluSTi makes three contributions.…”

Section: Table Structure Recognition Modelsmentioning

confidence: 99%

“…A Zucker [107] Character Region Awareness for Text Detection (CRAFT) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)…”

Section: Requires Little To No Involvement Of Domain Expertsmentioning

confidence: 99%

Deep learning for table detection and structure recognition: A survey

Kasem¹,

Abdallah²,

Berendeyev³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In our case, we do not need any complicated rules for table construction, as the mat Newer studies use various methods to find interesting cells, such as clustering [5], neural networks [6] or graph structures [7].…”

Section: Related Workmentioning

confidence: 99%

Automation of Basketball Match Data Management

Chomatek

Sierakowska

2021

Information

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Despite the fact that sport plays a substantial role in people’s lives, funding varies significantly from one discipline to another. For example, in Poland, women’s basketball in the lower divisions, is primarily developing thanks to enthusiasts. The aim of the work was to design and implement a system for analyzing match protocols containing data about the match. Particular attention was devoted to the course of the game, i.e., the order of scoring points. This type of data is not typically stored on the official websites of basketball associations but is significant from the point of view of coaches. The obtained data can be utilized to analyze the team’s game during the season, the quality of players, etc. In terms of obtaining data from match protocols, a dedicated algorithm for identifying the table was used, while a neural network was utilized to recognize the numbers (with 70% accuracy). The conducted research has shown the proposed system is well suited for data acquisition based on match protocols what implies the possibility of increasing the availability of data on the games. This will allow the development of this sport discipline. Obtained conclusions can be generalized to other disciplines, where the games are recorded in paper form.

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“…For table structure recognition algorithms, traditional computer vision algorithms restore table structures through graphic denoising and frame line recognition [36,37], in addition to structure-aware methods that reconstruct table structures by calculating the spatial relationships of cells [34,38]. These approaches tend to design specific structure recognition rules, resulting in limited generalization capabilities.…”

Section: Introductionmentioning

confidence: 99%

GeoKnowledgeFusion: A Platform for Multimodal Data Compilation from Geoscience Literature

Guo,

Wang,

Zhou

et al. 2024

Remote Sensing

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With the advent of big data science, the field of geoscience has undergone a paradigm shift toward data-driven scientific discovery. However, the abundance of geoscience data distributed across multiple sources poses significant challenges to researchers in terms of data compilation, which includes data collection, collation, and database construction. To streamline the data compilation process, we present GeoKnowledgeFusion, a publicly accessible platform for the fusion of text, visual, and tabular knowledge extracted from the geoscience literature. GeoKnowledgeFusion leverages a powerful network of models that provide a joint multimodal understanding of text, image, and tabular data, enabling researchers to efficiently curate and continuously update their databases. To demonstrate the practical applications of GeoKnowledgeFusion, we present two scenarios: the compilation of Sm-Nd isotope data for constructing a domain-specific database and geographic analysis, and the data extraction process for debris flow disasters. The data compilation process for these use cases encompasses various tasks, including PDF pre-processing, target element recognition, human-in-the-loop annotation, and joint multimodal knowledge understanding. The findings consistently reveal patterns that align with manually compiled data, thus affirming the credibility and dependability of our automated data processing tool. To date, GeoKnowledgeFusion has supported forty geoscience research teams within the program by processing over 40,000 documents uploaded by geoscientists.

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ClusTi: Clustering Method for Table Structure Recognition in Scanned Images

Cited by 8 publications

References 23 publications

Deep learning for table detection and structure recognition: A survey

Deep learning for table detection and structure recognition: A survey

Automation of Basketball Match Data Management

GeoKnowledgeFusion: A Platform for Multimodal Data Compilation from Geoscience Literature

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