Table Recognition
21 papers with code • 5 benchmarks • 5 datasets
Table recognition refers to the process of automatically identifying and extracting tabular structures from unstructured data sources such as text documents, images, or scanned documents. The goal of table recognition is to accurately detect the presence of tables within the data and extract their contents, including rows, columns, headers, and cell values.
Benchmarks
These leaderboards are used to track progress in Table Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Table Recognition models and implementations
PubTabNet
WTW
PubTables-1M
TNCR Dataset
Most implemented papers
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
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.
Flexible Table Recognition and Semantic Interpretation System
Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method.
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
xuewenyuan/TGRNet
•
•
ICCV 2021
A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research.
Detecting Layout Templates in Complex Multiregion Files
We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions.
PP-StructureV2: A Stronger Document Analysis System
For Table Recognition model, we utilize PP-LCNet, CSP-PAN and SLAHead to optimize the backbone module, feature fusion module and decoding module, respectively, which improved the table structure accuracy by 6\% with comparable inference speed.
LORE: Logical Location Regression Network for Table Structure Recognition
alibabaresearch/advancedliteratemachinery
•
•
Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
Rethinking Image-based Table Recognition Using Weakly Supervised Methods
In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images.
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
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.
A large-scale dataset for end-to-end table recognition in the wild
To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research.
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
alibabaresearch/advancedliteratemachinery
•
•
Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions.
