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
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context—0
Guided Table Structure Recognition through Anchor Optimization—0
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX—0
Neural Collaborative Graph Machines for Table Structure Recognition—0
Ontology-driven Information Extraction—0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
Robust Table Detection and Structure Recognition from Heterogeneous Document Images—0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
Split, embed and merge: An accurate table structure recognizer—0
Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach—0
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content—0
The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses—0
TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers—0
TSRFormer: Table Structure Recognition with Transformers—0
Synthesizing Realistic Data for Table RecognitionCode0
Flexible Table Recognition and Semantic Interpretation SystemCode0
PdfTable: A Unified Toolkit for Deep Learning-Based Table ExtractionCode0
PP-StructureV2: A Stronger Document Analysis SystemCode0
A Review On Table Recognition Based On Deep LearningCode0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language ModelsCode0
Rethinking Table Recognition using Graph Neural NetworksCode0
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table RecognitionCode0
OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table RecognitionCode0
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask AlignmentCode0
LORE: Logical Location Regression Network for Table Structure RecognitionCode0
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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