SOTAVerified

Document Image Classification

Document image classification is the task of classifying documents based on images of their contents.

( Image credit: Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 50 papers

TitleStatusHype
OCR-free Document Understanding TransformerCode3
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document UnderstandingCode2
BEiT: BERT Pre-Training of Image TransformersCode2
LayoutLM: Pre-training of Text and Layout for Document Image UnderstandingCode2
Multimodal Side-Tuning for Document ClassificationCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
DocXClassifier: High Performance Explainable Deep Network for Document Image ClassificationCode1
DiT: Self-supervised Pre-training for Document Image TransformerCode1
DocFormer: End-to-End Transformer for Document UnderstandingCode1
Revisiting ResNets: Improved Training and Scaling StrategiesCode1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
Training data-efficient image transformers & distillation through attentionCode1
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
RoBERTa: A Robustly Optimized BERT Pretraining ApproachCode1
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models—0
DoPTA: Improving Document Layout Analysis using Patch-Text Alignment—0
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
Multimodal Adaptive Inference for Document Image Classification with Anytime Early ExitingCode0
CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification—0
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding—0
Automatic Recognition of Learning Resource Category in a Digital LibraryCode0
SUT: a new multi-purpose synthetic dataset for Farsi document image analysisCode0
A Multi-Modal Multilingual Benchmark for Document Image Classification—0
GlobalDoc: A Cross-Modal Vision-Language Framework for Real-World Document Image Retrieval and Classification—0
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All datasetsRVL-CDIPTobacco-3482Noisy Bangla CharactersNoisy Bangla NumeralAIPN-MNISTNoisy MNISTSUT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EAMLAccuracy97.7—Unverified
2Cross-ModalAccuracy97.05—Unverified
3DocFormerBASEAccuracy96.17—Unverified
4LayoutLMV3LargeAccuracy95.93—Unverified
5LiLT[EN-R]BASEAccuracy95.68—Unverified
6LayoutLMv2LARGEAccuracy95.64—Unverified
7TILT-LargeAccuracy95.52—Unverified
8DocFormer largeAccuracy95.5—Unverified
9LayoutLMv3BASEAccuracy95.44—Unverified
10DonutAccuracy95.3—Unverified
#ModelMetricClaimedVerifiedStatus
1DocXClassifier-LAccuracy95.57—Unverified
2DocBert [DOCBERT]Accuracy91.95—Unverified
3Eff-GNN + Word2Vec [word2vec]Accuracy91—Unverified
4Multimodal Side-Tuning (MobileNetV2)Accuracy90.5—Unverified
5Multimodal Side-Tuning (ResNet50)Accuracy90.3—Unverified
6DocBERT [DOCBERT]Accuracy82.3—Unverified
7BERT [BERT]Accuracy79—Unverified
8Eff-GNN + Word2Vec [word2vec] + Image EmbeddingAccuracy77.5—Unverified
9Eff-GNN+ Word2Vec [word2vec]Accuracy73.5—Unverified
10VGGMemory7.08—Unverified
#ModelMetricClaimedVerifiedStatus
1PCGAN-CHARAccuracy89.54—Unverified
2Pixel-level RCAccuracy77.22—Unverified
#ModelMetricClaimedVerifiedStatus
1PCGAN-CHARAccuracy96.68—Unverified
2Pixel-level RCAccuracy95.46—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-RS (ResNet-200 + RS training tricks)Top 1 Accuracy - Verb83.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Pixel-level RCAccuracy97.62—Unverified
#ModelMetricClaimedVerifiedStatus
1PCGAN-CHARAccuracy98.43—Unverified
#ModelMetricClaimedVerifiedStatus
1CNNAccuracy86—Unverified