SOTAVerified

Key Information Extraction

Key Information Extraction (KIE) is aimed at extracting structured information (e.g. key-value pairs) from form-style documents (e.g. invoices), which makes an important step towards intelligent document understanding.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–74 of 74 papers

TitleStatusHype
NCU1415 at ROCLING 2022 Shared Task: A light-weight transformer-based approach for Biomedical Name Entity Recognition—0
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
PP-StructureV2: A Stronger Document Analysis SystemCode0
Key Information Extraction in Purchase Documents using Deep Learning and Rule-based Corrections—0
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural NetworksCode1
RDU: A Region-based Approach to Form-style Document Understanding—0
Relational Representation Learning in Visually-Rich Documents—0
LayoutLMv3: Pre-training for Document AI with Unified Text and Image MaskingCode0
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document UnderstandingCode2
Comparison of biomedical relationship extraction methods and models for knowledge graph creation—0
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents—0
One-shot Key Information Extraction from Document with Deep Partial Graph Matching—0
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and UnderstandingCode0
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and DissertationsCode0
Key Information Extraction From Documents: Evaluation And GeneratorCode1
ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents—0
Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts—0
Spatial Dual-Modality Graph Reasoning for Key Information ExtractionCode0
ICDAR2019 Competition on Scanned Receipt OCR and Information ExtractionCode0
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document UnderstandingCode0
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional NetworksCode1
LAMBERT: Layout-Aware (Language) Modeling for information extractionCode1
LayoutLM: Pre-training of Text and Layout for Document Image UnderstandingCode2
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All datasetsCORDSROIEKleister NDASIMARA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RORE (GeoLayoutLM)F198.52—Unverified
2GeoLayoutLMF197.97—Unverified
3LayoutLMv3 LargeF197.46—Unverified
4LayoutMask (large)F197.19—Unverified
5LayoutMask (base)F196.99—Unverified
6TPP (LayoutMask)F196.92—Unverified
7LILTF196.07—Unverified
8LayoutLMv2LARGEF196.01—Unverified
9LayoutLMv2BASEF194.95—Unverified
#ModelMetricClaimedVerifiedStatus
1LayoutLMv2LARGE (Excluding OCR mismatch)F197.81—Unverified
2RORE (GeoLayoutLM)F196.97—Unverified
3LayoutLMv2LARGEF196.61—Unverified
4LayoutLMv2BASEF196.25—Unverified
5ChatGPT 3.5 SpatialFormatAccuracy77—Unverified
#ModelMetricClaimedVerifiedStatus
1LayoutLMv2LARGEF185.2—Unverified
2LayoutLMv2BASEF183.3—Unverified
3LAMBERT (75M)F180.42—Unverified
#ModelMetricClaimedVerifiedStatus
1DANF1 (%)95.05—Unverified