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
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding—0
NCU1415 at ROCLING 2022 Shared Task: A light-weight transformer-based approach for Biomedical Name Entity Recognition—0
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition—0
OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table Recognition—0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models—0
One-shot Key Information Extraction from Document with Deep Partial Graph Matching—0
PaddleOCR 3.0 Technical Report—0
PDFVQA: A New Dataset for Real-World VQA on PDF Documents—0
PP-StructureV2: A Stronger Document Analysis System—0
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction—0
RDU: A Region-based Approach to Form-style Document Understanding—0
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document UnderstandingCode0
LayoutLMv3: Pre-training for Document AI with Unified Text and Image MaskingCode0
GraphRevisedIE: Multimodal Information Extraction with Graph-Revised NetworkCode0
Information Redundancy and Biases in Public Document Information Extraction BenchmarksCode0
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form ParserCode0
Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural NetworkCode0
Multimodal weighted graph representation for information extraction from visually rich documents.Code0
AMuRD: Annotated Arabic-English Receipt Dataset for Key Information Extraction and ClassificationCode0
DoSA : A System to Accelerate Annotations on Business Documents with Human-in-the-LoopCode0
Different Tastes of Entities: Investigating Human Label Variation in Named Entity AnnotationsCode0
Class-Agnostic Region-of-Interest Matching in Document ImagesCode0
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and DissertationsCode0
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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