Table Structure Recognition with Conditional Attention

• URL: http://arxiv.org/abs/2203.03819v1
• Date: Tue, 8 Mar 2022 02:44:58 GMT
• Title: Table Structure Recognition with Conditional Attention
• Authors: Bin Xiao, Murat Simsek, Burak Kantarci and Ala Abu Alkheir
• Abstract summary: Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format. In this study, we hypothesize that a complicated table structure can be represented by a graph whose vertices and edges represent the cells and association between cells, respectively. Experimental results show that the alignment of a cell bounding box can help improve the Micro-averaged F1 score from 0.915 to 0.963, and the Macro-average F1 score from 0.787 to 0.923.
• Score: 13.976736586808308
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tabular data in digital documents is widely used to express compact and important information for readers. However, it is challenging to parse tables from unstructured digital documents, such as PDFs and images, into machine-readable format because of the complexity of table structures and the missing of meta-information. Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format so that the tabular data can be further analysed by the down-stream tasks, such as semantic modeling and information retrieval. In this study, we hypothesize that a complicated table structure can be represented by a graph whose vertices and edges represent the cells and association between cells, respectively. Then we define the table structure recognition problem as a cell association classification problem and propose a conditional attention network (CATT-Net). The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods on various datasets. Besides, we investigate whether the alignment of a cell bounding box or a text-focused approach has more impact on the model performance. Due to the lack of public dataset annotations based on these two approaches, we further annotate the ICDAR2013 dataset providing both types of bounding boxes, which can be a new benchmark dataset for evaluating the methods in this field. Experimental results show that the alignment of a cell bounding box can help improve the Micro-averaged F1 score from 0.915 to 0.963, and the Macro-average F1 score from 0.787 to 0.923.

Related papers

• UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition [55.153629718464565]
  We introduce UniTabNet, a novel framework for table structure parsing based on the image-to-text model. UniTabNet employs a divide-and-conquer'' strategy, utilizing an image-to-text model to decouple table cells and integrating both physical and logical decoders to reconstruct the complete table structure.
  arXiv  Detail & Related papers  (2024-09-20T01:26:32Z)
• Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs [79.0426838808629]
  We propose TAT-DQA, i.e. to answer the question over a visually-rich table-text document. Specifically, we propose a novel Doc2SoarGraph framework with enhanced discrete reasoning capability. We conduct extensive experiments on TAT-DQA dataset, and the results show that our proposed framework outperforms the best baseline model by 17.73% and 16.91% in terms of Exact Match (EM) and F1 score respectively on the test set.
  arXiv  Detail & Related papers  (2023-05-03T07:30:32Z)
• SEMv2: Table Separation Line Detection Based on Instance Segmentation [96.36188168694781]
  We propose an accurate table structure recognizer, termed SEMv2 (SEM: Split, Embed and Merge) We address the table separation line instance-level discrimination problem and introduce a table separation line detection strategy based on conditional convolution. To comprehensively evaluate the SEMv2, we also present a more challenging dataset for table structure recognition, dubbed iFLYTAB.
  arXiv  Detail & Related papers  (2023-03-08T05:15:01Z)
• TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers [56.56591337457137]
  We propose an accurate and end-to-end transformer-based table structure recognition method, referred to as TRUST. Transformers are suitable for table structure recognition because of their global computations, perfect memory, and parallel computation. We conduct experiments on several popular benchmarks including PubTabNet and SynthTable, our method achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-08-31T08:33:36Z)
• Visual Understanding of Complex Table Structures from Document Images [32.95187519339354]
  We propose a novel object-detection-based deep model that captures the inherent alignments of cells within tables. We also aim to improve structure recognition by deducing a novel rectilinear graph-based formulation. Our framework improves the previous state-of-the-art performance by a 2.7% average F1-score on benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-13T14:54:33Z)
• Split, embed and merge: An accurate table structure recognizer [42.579215135672094]
  We introduce Split, Embed and Merge (SEM) as an accurate table structure recognizer. SEM can achieve an average F-Measure of $96.9%$ on the SciTSR dataset.
  arXiv  Detail & Related papers  (2021-07-12T06:26:19Z)
• TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition [76.06530816349763]
  We propose an end-to-end trainable table graph reconstruction network (TGRNet) for table structure recognition. Specifically, the proposed method has two main branches, a cell detection branch and a cell logical location branch, to jointly predict the spatial location and the logical location of different cells.
  arXiv  Detail & Related papers  (2021-06-20T01:57:05Z)
• TCN: Table Convolutional Network for Web Table Interpretation [52.32515851633981]
  We propose a novel table representation learning approach considering both the intra- and inter-table contextual information. Our method can outperform competitive baselines by +4.8% of F1 for column type prediction and by +4.1% of F1 for column pairwise relation prediction.
  arXiv  Detail & Related papers  (2021-02-17T02:18:10Z)
• Table Structure Recognition using Top-Down and Bottom-Up Cues [28.65687982486627]
  We present an approach for table structure recognition that combines cell detection and interaction modules. We empirically validate our method on the publicly available real-world datasets.
  arXiv  Detail & Related papers  (2020-10-09T13:32:53Z)
• Identifying Table Structure in Documents using Conditional Generative Adversarial Networks [0.0]
  In many industries and in academic research, information is primarily transmitted in the form of unstructured documents. We propose a top-down approach, first using a conditional generative adversarial network to map a table image into a standardised skeleton' table form. We then deriving latent table structure using xy-cut projection and Genetic Algorithm optimisation.
  arXiv  Detail & Related papers  (2020-01-13T20:42:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.