Document Layout Analysis2020en
publaynet-val
Dataset from Papers With Code
Metrics:accuracy, cer, wer, f1
figure
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | DETR | 0.975 | Jun 2023 | |
| 2 | DiT-L | 0.972 | Mar 2022 | |
| 3 | VGT | 0.971 | Aug 2023 | |
| 4 | DoPTA | 0.970 | Dec 2024 | |
| 5 | LayoutLMv3-B | 0.970 | Apr 2022 | |
| 6 | ResNext-101-32×8d | 0.968 | Aug 2023 | |
| 7 | TRDLU | 0.966 | Transformer-based Approach for Document Understanding | Oct 2022 |
| 8 | VSR | 0.964 | May 2021 | |
| 9 | UDoc | 0.964 | Apr 2022 | |
| 10 | DeiT-BOpen Source Meta | 0.957 | Dec 2020 | |
| 11 | BEiT-B | 0.957 | Jun 2021 | |
| 12 | Mask RCNN | 0.949 | Aug 2019 | |
| 13 | Faster_RCNN | 0.937 | Aug 2019 | |
| 14 | GLAM | 0.206 | Aug 2023 |
list
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | TRDLU | 0.975 | Transformer-based Approach for Document Understanding | Oct 2022 |
| 2 | VGT | 0.968 | Aug 2023 | |
| 3 | DETR | 0.964 | Jun 2023 | |
| 4 | DiT-L | 0.960 | Mar 2022 | |
| 5 | DoPTA | 0.957 | Dec 2024 | |
| 6 | LayoutLMv3-B | 0.955 | Apr 2022 | |
| 7 | VSR | 0.947 | May 2021 | |
| 8 | ResNext-101-32×8d | 0.940 | Aug 2023 | |
| 9 | UDoc | 0.937 | Apr 2022 | |
| 10 | BEiT-B | 0.924 | Jun 2021 | |
| 11 | DeiT-BOpen Source Meta | 0.921 | Dec 2020 | |
| 12 | Mask RCNN | 0.886 | Aug 2019 | |
| 13 | Faster_RCNN | 0.883 | Aug 2019 | |
| 14 | GLAM | 0.862 | Aug 2023 |
overall
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | VGT | 0.962 | Aug 2023 | |
| 2 | TRDLU | 0.959 | Transformer-based Approach for Document Understanding | Oct 2022 |
| 3 | VSR | 0.957 | May 2021 | |
| 4 | DETR | 0.957 | Jun 2023 | |
| 5 | LayoutLMv3-B | 0.951 | Apr 2022 | |
| 6 | DiT-L | 0.949 | Mar 2022 | |
| 7 | DoPTA | 0.949 | Dec 2024 | |
| 8 | UDoc | 0.939 | Apr 2022 | |
| 9 | ResNext-101-32×8d | 0.935 | Aug 2023 | |
| 10 | DeiT-BOpen Source Meta | 0.932 | Dec 2020 | |
| 11 | BEiT-B | 0.931 | Jun 2021 | |
| 12 | Mask RCNN | 0.910 | Aug 2019 | |
| 13 | Faster_RCNN | 0.902 | Aug 2019 | |
| 14 | GLAM | 0.722 | Aug 2023 |
table
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | DETR | 0.981 | Jun 2023 | |
| 2 | VGT | 0.981 | Aug 2023 | |
| 3 | LayoutLMv3-B | 0.979 | Apr 2022 | |
| 4 | CDeC-Net | 0.978 | Aug 2020 | |
| 5 | DiT-L | 0.978 | Mar 2022 | |
| 6 | DoPTA | 0.977 | Dec 2024 | |
| 7 | ResNext-101-32×8d | 0.976 | Aug 2023 | |
| 8 | TRDLU | 0.976 | Transformer-based Approach for Document Understanding | Oct 2022 |
| 9 | VSR | 0.974 | May 2021 | |
| 10 | UDoc | 0.973 | Apr 2022 | |
| 11 | BEiT-B | 0.973 | Jun 2021 | |
| 12 | DeiT-BOpen Source Meta | 0.972 | Dec 2020 | |
| 13 | Mask RCNN | 0.960 | Aug 2019 | |
| 14 | Faster_RCNN | 0.954 | Aug 2019 | |
| 15 | GLAM | 0.868 | Aug 2023 |
text
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | VSR | 0.967 | May 2021 | |
| 2 | TRDLU | 0.958 | Transformer-based Approach for Document Understanding | Oct 2022 |
| 3 | VGT | 0.950 | Aug 2023 | |
| 4 | DETR | 0.947 | Jun 2023 | |
| 5 | LayoutLMv3-B | 0.945 | Apr 2022 | |
| 6 | DoPTA | 0.944 | Dec 2024 | |
| 7 | DiT-L | 0.944 | Mar 2022 | |
| 8 | UDoc | 0.939 | Apr 2022 | |
| 9 | BEiT-B | 0.934 | Jun 2021 | |
| 10 | DeiT-BOpen Source Meta | 0.934 | Dec 2020 | |
| 11 | ResNext-101-32×8d | 0.930 | Aug 2023 | |
| 12 | Mask RCNN | 0.916 | Aug 2019 | |
| 13 | Faster_RCNN | 0.910 | Aug 2019 | |
| 14 | GLAM | 0.878 | Aug 2023 |
title
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | VGT | 0.939 | Aug 2023 | |
| 2 | VSR | 0.931 | May 2021 | |
| 3 | TRDLU | 0.921 | Transformer-based Approach for Document Understanding | Oct 2022 |
| 4 | DETR | 0.918 | Jun 2023 | |
| 5 | LayoutLMv3-B | 0.906 | Apr 2022 | |
| 6 | DoPTA | 0.895 | Dec 2024 | |
| 7 | DiT-L | 0.893 | Mar 2022 | |
| 8 | UDoc | 0.885 | Apr 2022 | |
| 9 | DeiT-BOpen Source Meta | 0.874 | Dec 2020 | |
| 10 | BEiT-B | 0.866 | Jun 2021 | |
| 11 | ResNext-101-32×8d | 0.862 | Aug 2023 | |
| 12 | Mask RCNN | 0.840 | Aug 2019 | |
| 13 | Faster_RCNN | 0.826 | Aug 2019 | |
| 14 | GLAM | 0.800 | Aug 2023 |
Related Papers12
DoPTA: Improving Document Layout Analysis using Patch-Text Alignment
Dec 2024Models: DoPTA
Vision Grid Transformer for Document Layout Analysis
Aug 2023Models: VGT, ResNext-101-32×8d
A Graphical Approach to Document Layout Analysis
Aug 2023Models: GLAM
Unified Pretraining Framework for Document Understanding
Apr 2022Models: UDoc
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Apr 2022Models: LayoutLMv3-B
DiT: Self-supervised Pre-training for Document Image Transformer
Mar 2022Models: DiT-L
BEiT: BERT Pre-Training of Image Transformers
Jun 2021Models: BEiT-B
Training data-efficient image transformers & distillation through attention
Dec 2020Models: DeiT-B
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
Aug 2020Models: CDeC-Net
PubLayNet: largest dataset ever for document layout analysis
Aug 2019Models: Mask RCNN, Faster_RCNN