Table Detection
20 papers with code • 3 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Table Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ICDAR 2013
FUNSD
SciTSR
PubTables-1M
TableBank
IIIT-AR-13K
TNCR Dataset
ICDAR 2019
STDW
Most implemented papers
TableBank: Table Benchmark for Image-based Table Detection and Recognition
We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet.
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.
TNCR: Table Net Detection and Classification Dataset
abdoelsayed2016/TNCR_Dataset
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Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone Network achieves the highest performance compared to other methods with a precision of 79. 7%, recall of 89. 8%, and f1 score of 84. 4% on the TNCR dataset.
TableSense: Spreadsheet Table Detection with Convolutional Neural Networks
Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges.
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis.
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method
In this paper, we introduce a diverse large-scale dataset for table detection with more than seven thousand samples containing a wide variety of table structures collected from many diverse sources.
CTE: A Dataset for Contextualized Table Extraction
We define the task of Contextualized Table Extraction (CTE), which aims to extract and define the structure of tables considering the textual context of the document.
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.
Table Detection for Visually Rich Document Images
Table Detection (TD) is a fundamental task to enable visually rich document understanding, which requires the model to extract information without information loss.
