# Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations

arXiv: https://arxiv.org/abs/2105.11021 · HTML: https://mlsanity.com/paper/2105.11021/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 5 of 5)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://mlsanity.com/paper/2004.12629/) (2020)

## Built on by (most cited first, 9 of 9)

- [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://mlsanity.com/paper/2303.06949/) (2023)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [SPRINT: Script-agnostic Structure Recognition in Tables](https://mlsanity.com/paper/2503.11932/) (2025)
- [A Study on Reproducibility and Replicability of Table Structure Recognition Methods](https://mlsanity.com/paper/2304.10439/) (2023)
- [PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents](https://mlsanity.com/paper/2403.15724/) (2024)
- [PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction](https://mlsanity.com/paper/2409.05125/) (2024)
- [Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation](https://mlsanity.com/paper/2505.18925/) (2025)
- [Predicting the Past: Estimating Historical Appraisals with OCR and Machine Learning](https://mlsanity.com/paper/2505.24676/) (2025)
- [EMBLEM: Enhancing Multi-script Table Detection through Masking](https://mlsanity.com/paper/2609.08330/) (2026)

## Abstract (arXiv)

As global trends are shifting towards data-driven industries, the demand for automated algorithms that can convert digital images of scanned documents into machine readable information is rapidly growing. Besides the opportunity of data digitization for the application of data analytic tools, there is also a massive improvement towards automation of processes, which previously would require manual inspection of the documents. Although the introduction of optical character recognition technologies mostly solved the task of converting human-readable characters from images into machine-readable characters, the task of extracting table semantics has been less focused on over the years. The recognition of tables consists of two main tasks, namely table detection and table structure recognition. Most prior work on this problem focuses on either task without offering an end-to-end solution or paying attention to real application conditions like rotated images or noise artefacts inside the document image. Recent work shows a clear trend towards deep learning approaches coupled with the use of transfer learning for the task of table structure recognition due to the lack of sufficiently large datasets. In this paper we present a multistage pipeline named Multi-Type-TD-TSR, which offers an end-to-end solution for the problem of table recognition. It utilizes state-of-the-art deep learning models for table detection and differentiates between 3 different types of tables based on the tables' borders. For the table structure recognition we use a deterministic non-data driven algorithm, which works on all table types. We additionally present two algorithms. One for unbordered tables and one for bordered tables, which are the base of the used table structure recognition algorithm. We evaluate Multi-Type-TD-TSR on the ICDAR 2019 table structure recognition dataset and achieve a new state-of-the-art.