Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Datasets
Greatest papers with code
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents
• DevashishPrasad/CascadeTabNet
• 
In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.
Ranked #1 on
Table Detection
on ICDAR2013
IMAGE AUGMENTATION TABLE DETECTION TABLE RECOGNITION TRANSFER LEARNING
TableBank: A Benchmark Dataset for 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.
The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images
A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding.
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
• mdv3101/CDeCNet
•
Localizing page elements/objects such as tables, figures, equations, etc.
Ranked #1 on
Table Detection
on ICDAR2013
A framework for information extraction from tables in biomedical literature
The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications.
TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images
This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table.
Ranked #2 on
Table Detection
on ICDAR2013
Table understanding in structured documents
• Darthholi/similarity-models
• 
We will work on our newly presented dataset of pro forma invoices, invoices and debit note documents using this representation and propose multiple baselines to solve this labeling problem.