Matches in SemOpenAlex for { <https://semopenalex.org/work/W3159432511> ?p ?o ?g. }
- W3159432511 abstract "The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recognition are pivotal problems in the domain of table understanding. However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables. Therefore, it is an active area of research in document image analysis. Recent advances in the computing capabilities of graphical processing units have enabled deep neural networks to outperform traditional state-of-the-art machine learning methods. Table understanding has substantially benefited from the recent breakthroughs in deep neural networks. However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition. This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks. This work provided a thorough understanding of the current state-of-the-art and related challenges of table understanding in document images. Furthermore, the leading datasets and their intricacies have been elaborated along with the quantitative results. Moreover, a brief overview is given regarding the promising directions that can serve as a guide to further improve table analysis in document images." @default.
- W3159432511 created "2021-05-10" @default.
- W3159432511 creator A5035342598 @default.
- W3159432511 creator A5051437282 @default.
- W3159432511 creator A5051650277 @default.
- W3159432511 creator A5060268056 @default.
- W3159432511 creator A5073619925 @default.
- W3159432511 creator A5086894159 @default.
- W3159432511 date "2021-04-29" @default.
- W3159432511 modified "2023-09-27" @default.
- W3159432511 title "Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks." @default.
- W3159432511 cites W1525954826 @default.
- W3159432511 cites W1536680647 @default.
- W3159432511 cites W1555624686 @default.
- W3159432511 cites W1568702397 @default.
- W3159432511 cites W1660929446 @default.
- W3159432511 cites W1710476689 @default.
- W3159432511 cites W1849277567 @default.
- W3159432511 cites W1861492603 @default.
- W3159432511 cites W1901129140 @default.
- W3159432511 cites W1903029394 @default.
- W3159432511 cites W1924770834 @default.
- W3159432511 cites W1971336721 @default.
- W3159432511 cites W1988783898 @default.
- W3159432511 cites W2009659675 @default.
- W3159432511 cites W2022351003 @default.
- W3159432511 cites W2024915064 @default.
- W3159432511 cites W2029611516 @default.
- W3159432511 cites W2031489346 @default.
- W3159432511 cites W2041995828 @default.
- W3159432511 cites W2046941907 @default.
- W3159432511 cites W2051265407 @default.
- W3159432511 cites W2055540722 @default.
- W3159432511 cites W2064675550 @default.
- W3159432511 cites W2078206655 @default.
- W3159432511 cites W2090923791 @default.
- W3159432511 cites W2092772700 @default.
- W3159432511 cites W2098218583 @default.
- W3159432511 cites W2101105183 @default.
- W3159432511 cites W2105693220 @default.
- W3159432511 cites W2107092590 @default.
- W3159432511 cites W2116341502 @default.
- W3159432511 cites W2117539524 @default.
- W3159432511 cites W2119014534 @default.
- W3159432511 cites W2119821739 @default.
- W3159432511 cites W2121061496 @default.
- W3159432511 cites W2136379584 @default.
- W3159432511 cites W2145339207 @default.
- W3159432511 cites W2150673968 @default.
- W3159432511 cites W2156332201 @default.
- W3159432511 cites W2161236525 @default.
- W3159432511 cites W2163605009 @default.
- W3159432511 cites W2166323498 @default.
- W3159432511 cites W2168459394 @default.
- W3159432511 cites W2170607218 @default.
- W3159432511 cites W2207576364 @default.
- W3159432511 cites W2280985394 @default.
- W3159432511 cites W2321821989 @default.
- W3159432511 cites W2395611524 @default.
- W3159432511 cites W2444353601 @default.
- W3159432511 cites W2518276024 @default.
- W3159432511 cites W2519887557 @default.
- W3159432511 cites W2521665229 @default.
- W3159432511 cites W2549139847 @default.
- W3159432511 cites W2601564443 @default.
- W3159432511 cites W2613718673 @default.
- W3159432511 cites W2763323349 @default.
- W3159432511 cites W2777421064 @default.
- W3159432511 cites W2786162033 @default.
- W3159432511 cites W2786515133 @default.
- W3159432511 cites W2787523828 @default.
- W3159432511 cites W2787835872 @default.
- W3159432511 cites W2796347433 @default.
- W3159432511 cites W2797320628 @default.
- W3159432511 cites W2901890385 @default.
- W3159432511 cites W2911283634 @default.
- W3159432511 cites W2913389685 @default.
- W3159432511 cites W2928165649 @default.
- W3159432511 cites W2962772269 @default.
- W3159432511 cites W2962835968 @default.
- W3159432511 cites W2963037989 @default.
- W3159432511 cites W2963150697 @default.
- W3159432511 cites W2963179609 @default.
- W3159432511 cites W2963212250 @default.
- W3159432511 cites W2963311793 @default.
- W3159432511 cites W2963336383 @default.
- W3159432511 cites W2963351448 @default.
- W3159432511 cites W2963840672 @default.
- W3159432511 cites W2963849369 @default.
- W3159432511 cites W2964241181 @default.
- W3159432511 cites W2968868378 @default.
- W3159432511 cites W2971712385 @default.
- W3159432511 cites W2979750740 @default.
- W3159432511 cites W298212978 @default.
- W3159432511 cites W2986271972 @default.
- W3159432511 cites W2990963180 @default.
- W3159432511 cites W2998228095 @default.
- W3159432511 cites W2998913931 @default.
- W3159432511 cites W2999605892 @default.
- W3159432511 cites W3003206728 @default.