SOTAVerified

Table Recognition

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 50 papers

TitleStatusHype
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised PretrainingCode4
Aligning benchmark datasets for table structure recognitionCode4
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
PubTables-1M: Towards comprehensive table extraction from unstructured documentsCode2
High-Performance Transformers for Table Structure Recognition Need Early ConvolutionsCode2
ICDAR 2021 Competition on Scientific Literature ParsingCode2
Table Structure Recognition using Top-Down and Bottom-Up CuesCode1
Deep learning for table detection and structure recognition: A surveyCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTMLCode1
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain ReasonerCode1
A large-scale dataset for end-to-end table recognition in the wildCode1
Rethinking Image-based Table Recognition Using Weakly Supervised MethodsCode1
Image-based table recognition: data, model, and evaluationCode1
TGRNet: A Table Graph Reconstruction Network for Table Structure RecognitionCode1
Multi-Cell Decoder and Mutual Learning for Table Structure and Character RecognitionCode1
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 RepresentationsCode1
Detecting Layout Templates in Complex Multiregion FilesCode1
VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction—0
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables—0
Benchmarking Table Comprehension In The Wild—0
Comparing Machine Learning Approaches for Table Recognition in Historical Register Books—0
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks—0
Detecting Table Region in PDF Documents Using Distant Supervision—0
Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types—0
Show:102550
← PrevPage 1 of 2Next →
All datasetsPubTabNetTable Recognition Challenge mini-testTable Recognition Challenge testICDAR2013 table structure recognitionWTW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TSRFormerTEDS-Struct97.5—Unverified
2RTSRTEDS-Struct97—Unverified
3MuTabNetTEDS (all samples)96.87—Unverified
4TableMasterTEDS (all samples)96.76—Unverified
5Multi-Task Learning ModelTEDS (all samples)96.67—Unverified
6ConvStemTEDS (all samples)96.53—Unverified
7SLANetTEDS (all samples)96.3—Unverified
8TRUSTTEDS (all samples)96.2—Unverified
9NCGMTEDS (all samples)95.4—Unverified
10LGPMATEDS (all samples)94.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Re0TEDS (all samples)95.66—Unverified
2VCGroupTEDS (all samples)95.04—Unverified
3Habitat-WebTEDS (all samples)89.97—Unverified
4EDDTEDS (all samples)89.97—Unverified
#ModelMetricClaimedVerifiedStatus
1Habitat-WebTEDS (all samples)89.18—Unverified
2EDDTEDS (all samples)89.18—Unverified
#ModelMetricClaimedVerifiedStatus
1Proposed System (With post- processing)F-Measure95.46—Unverified
#ModelMetricClaimedVerifiedStatus
1StrucTexTv2 (small)F178.9—Unverified