Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach

Adiga, Darshan; Bhat, Shabir Ahmad; Shah, Muzaffar Bashir; Vyeth, Viveka

doi:10.26615/978-954-452-056-4_001

Cited by 6 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of course, if a table has a very complex structure, there may be more such misinterpretations of its structure. Newer studies use various methods to find interesting cells, such as clustering [ neural networks [6] or graph structures [7].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automation of Basketball Match Data Management

Chomatek

Sierakowska

2021

Information

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…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

View full text Add to dashboard Cite

show abstract

“…These methods make strong assumptions about table layouts for a domain agnostic algorithm. Many cognitive methods [6,7,8,9,10,11,12,14,15,16,37,38,39,40,41,42,43] have also been presented to understand table structures as they are robust to the input type (whether being scanned images or native digital). These also do not make any assumptions about the layouts, are data-driven, and are easy to fine-tune across different domains.…”

Section: Related Workmentioning

confidence: 99%

Table Structure Recognition using Top-Down and Bottom-Up Cues

Raja

Mondal

Jawahar

2020

Preprint

View full text Add to dashboard Cite

Tables are information-rich structured objects in document images. While significant work has been done in localizing tables as graphic objects in document images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from the pdf document or on the optical character recognition (ocr) models to extract low-level layout features from the image. However, these methods fail to generalize well because of the absence of meta-features or errors made by the ocr when there is a significant variance in table layouts and text organization. In our work, we focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or ocr.We present an approach for table structure recognition that combines cell detection and interaction modules to localize the cells and predict their row and column associations with other detected cells. We incorporate structural constraints as additional differential components to the loss function for cell detection. We empirically validate our method on the publicly available real-world datasetsicdar-2013, icdar-2019 (ctdar) archival, unlv, scitsr, scitsr-comp, tablebank, and pubtabnet. Our attempt opens up a new direction for table structure recognition by combining top-down (table cells detection) and bottom-up (structure recognition) cues in visually understanding the tables.

show abstract