mlsanityData to 7 Oct 2026

An End-to-End Multi-Task Learning Model for Image-based Table Recognition

arXiv 2303.08648 ↗First posted 15 Mar 2023cs.CVcited by 14

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Abstract (arXiv)

Image-based table recognition is a challenging task due to the diversity of table styles and the complexity of table structures. Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each sub-problem independently using two separate systems. In this paper, we propose an end-to-end multi-task learning model for image-based table recognition. The proposed model consists of one shared encoder, one shared decoder, and three separate decoders which are used for learning three sub-tasks of table recognition: table structure recognition, cell detection, and cell-content recognition. The whole system can be easily trained and inferred in an end-to-end approach. In the experiments, we evaluate the performance of the proposed model on two large-scale datasets: FinTabNet and PubTabNet. The experiment results show that the proposed model outperforms the state-of-the-art methods in all benchmark datasets.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

18 of 30 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2019Image-based table recognition: data, model, and evaluationcited by 233
  6. 2015End-to-End Attention-based Large Vocabulary Speech Recognitioncited by 148
  7. 2020Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Contextcited by 146
  8. 2022TableFormer: Table Structure Understanding with Transformerscited by 93
  9. 2016Image-to-Markup Generation with Coarse-to-Fine Attentioncited by 79
  10. 2019MASTER: Multi-Aspect Non-local Network for Scene Text Recognitioncited by 65
  11. 2020Table Structure Recognition using Top-Down and Bottom-Up Cuescited by 53
  12. 2021LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignmentcited by 46
6 more
  1. 2021Split, embed and merge: An accurate table structure recognizercited by 42
  2. 2021PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTMLcited by 39
  3. 2021ICDAR 2021 Competition on Scientific Literature Parsingcited by 16
  4. 2021PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latexcited by 10
  5. 2021ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeXcited by 9
  6. 2019The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Imagescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 2; 2026: 5.

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  1. 2024PaliGemma 2: A Family of Versatile VLMs for Transfercited by 204
  2. 2024Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognitioncited by 5
  3. 2024Vision Language Models for Spreadsheet Understanding: Challenges and Opportunitiescited by 4
  4. 2024CREPE: Coordinate-Aware End-to-End Document Parsercited by 3
  5. 2023Rethinking Detection Based Table Structure Recognition for Visually Rich Document Imagescited by 1
  6. 2024PdfTable: A Unified Toolkit for Deep Learning-Based Table Extractioncited by 1
  7. 2025Title block detection and information extraction for enhanced building drawings searchcited by 1
  8. 2026InstructTable: Improving Table Structure Recognition Through Instructionscited by 1
  9. 2023TabIQA: Table Questions Answering on Business Document Imagescited by 0
  10. 2025Hierarchical Modeling Approach to Fast and Accurate Table Recognitioncited by 0
  11. 2026TableSeq: Unified Generation of Structure, Content, and Layoutcited by 0
  12. 2026FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformerscited by 0
2 more
  1. 2026Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representationscited by 0
  2. 2026Tables Decoded: DELTA for Structure, TARQA for Understandingcited by 0