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 26–50 of 50 papers

TitleStatusHype
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
PubTables-1M: Towards comprehensive table extraction from unstructured documentsCode2
Detecting Layout Templates in Complex Multiregion FilesCode1
Split, embed and merge: An accurate table structure recognizer—0
TGRNet: A Table Graph Reconstruction Network for Table Structure RecognitionCode1
ICDAR 2021 Competition on Scientific Literature ParsingCode2
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX—0
Flexible Table Recognition and Semantic Interpretation SystemCode0
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
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask AlignmentCode0
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTMLCode1
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks—0
Guided Table Structure Recognition through Anchor Optimization—0
Table Structure Recognition using Top-Down and Bottom-Up CuesCode1
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context—0
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
Image-based table recognition: data, model, and evaluationCode1
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