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 26–50 of 50 papers

TitleStatusHype
PdfTable: A Unified Toolkit for Deep Learning-Based Table ExtractionCode0
Rethinking Table Recognition using Graph Neural NetworksCode0
Synthesizing Realistic Data for Table RecognitionCode0
Flexible Table Recognition and Semantic Interpretation SystemCode0
TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers—0
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context—0
Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types—0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
Detecting Table Region in PDF Documents Using Distant Supervision—0
TSRFormer: Table Structure Recognition with Transformers—0
Robust Table Detection and Structure Recognition from Heterogeneous Document Images—0
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks—0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
Split, embed and merge: An accurate table structure recognizer—0
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables—0
VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction—0
Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach—0
Comparing Machine Learning Approaches for Table Recognition in Historical Register Books—0
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX—0
Guided Table Structure Recognition through Anchor Optimization—0
Neural Collaborative Graph Machines for Table Structure Recognition—0
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content—0
Benchmarking Table Comprehension In The Wild—0
The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses—0
Ontology-driven Information Extraction—0
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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