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

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Showing 1–50 of 50 papers

TitleStatusHype
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised PretrainingCode4
Aligning benchmark datasets for table structure recognitionCode4
High-Performance Transformers for Table Structure Recognition Need Early ConvolutionsCode2
PubTables-1M: Towards comprehensive table extraction from unstructured documentsCode2
ICDAR 2021 Competition on Scientific Literature ParsingCode2
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain ReasonerCode1
Multi-Cell Decoder and Mutual Learning for Table Structure and Character RecognitionCode1
A large-scale dataset for end-to-end table recognition in the wildCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Rethinking Image-based Table Recognition Using Weakly Supervised MethodsCode1
Deep learning for table detection and structure recognition: A surveyCode1
Detecting Layout Templates in Complex Multiregion FilesCode1
TGRNet: A Table Graph Reconstruction Network for Table Structure 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
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTMLCode1
Table Structure Recognition using Top-Down and Bottom-Up CuesCode1
Image-based table recognition: data, model, and evaluationCode1
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables—0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language ModelsCode0
Benchmarking Table Comprehension In The Wild—0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
PdfTable: A Unified Toolkit for Deep Learning-Based Table ExtractionCode0
VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction—0
The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses—0
Synthesizing Realistic Data for Table RecognitionCode0
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content—0
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table RecognitionCode0
Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types—0
OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table RecognitionCode0
A Review On Table Recognition Based On Deep LearningCode0
LORE: Logical Location Regression Network for Table Structure RecognitionCode0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
PP-StructureV2: A Stronger Document Analysis SystemCode0
TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers—0
TSRFormer: Table Structure Recognition with Transformers—0
Robust Table Detection and Structure Recognition from Heterogeneous Document Images—0
Neural Collaborative Graph Machines for Table Structure Recognition—0
Split, embed and merge: An accurate table structure recognizer—0
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX—0
Flexible Table Recognition and Semantic Interpretation SystemCode0
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask AlignmentCode0
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks—0
Guided Table Structure Recognition through Anchor Optimization—0
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context—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
Rethinking Table Recognition using Graph Neural NetworksCode0
Ontology-driven Information Extraction—0
Detecting Table Region in PDF Documents Using Distant Supervision—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