docling-project/MarkushGrapher-2
Image-to-Text • Updated • 39
id stringlengths 12 58 | page_image_path stringlengths 16 62 | annotation stringlengths 62 855 | cxsmiles_dataset stringlengths 15 915 | cxsmiles stringlengths 12 913 | cxsmiles_opt stringlengths 11 804 | cells listlengths 0 66 | page_image imagewidth (px) 1.02k 1.02k |
|---|---|---|---|---|---|---|---|
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1020107019907_33_0 | 1020107019907_33_0.png | <markush><cxsmi>COC1=NC2=C([V]=C1)<r>W</r>=CC(<r>R1</r>)=C2OC(C<r>NHBoc</r>)C<r>OMs</r></cxsmi><stable></stable></markush> | COC1=NC2=C([V]=C1)[1*]=CC([2*])=C2OC(C[3*])C[4*] |$;;;;;;;;W;;;R1;;;;;NHBoc;;OMs;$| | *CC(C*)OC1=C(*)C=*C2=C1N=C(OC)C=[V]2 |$NHBoc;;;;OMs;;;;R1;;W;;;;;;;;$,| | COC1=NC2=C([V]=C1)<r>W</r>=CC(<r>R1</r>)=C2OC(C<r>NHBoc</r>)C<r>OMs</r> | [
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EP08290074NWA1_9_1 | EP08290074NWA1_9_1.png | <markush><cxsmi><r>Pg</r>NC1CN(<r>R4</r>)CCN2C1=NC(<r>R1</r>)=C(<r>R2</r>)C2=O</cxsmi><stable></stable></markush> | [1*]NC1CN([2*])CCN2C1=NC([3*])=C([4*])C2=O |$Pg;;;;;R4;;;;;;;R1;;R2;;;$| | *NC1CN(*)CCN2C(=O)C(*)=C(*)N=C12 |$Pg;;;;;R4;;;;;;;R2;;R1;;$,| | <r>Pg</r>NC1CN(<r>R4</r>)CCN2C1=NC(<r>R1</r>)=C(<r>R2</r>)C2=O | [
{
"bbox": [
0.578125,
0.0439453125,
0.6748046875,
0.1103515625
],
"text": "R1"
},
{
"bbox": [
0.84375,
0.1943359375,
0.9501953125,
0.265625
],
"text": "R2"
},
{
"bbox": [
0.447265625,
0.2744140625,
0.5,
0.343... | |
US20220119590A1_29_3 | US20220119590A1_29_3.png | <markush><cxsmi>C<r>R</r>.C<r>R</r>.<r>R</r>C1(<r>Q</r>)C2=CC=CC(C3=CC=C(<r>_AP</r>)C=C3)=C2C2=C1C=CC=C2|Sg:n:1:p:ht,Sg:n:3:q:ht,m:0:7.8.9.10.11.19,m:2:20.21.22.23.24.25</cxsmi><stable></stable></markush> | C[1*].C[1*].[1*]C1([4*])C2=CC=CC(C3=CC=C([5*])C=C3)=C2C2=C1C=CC=C2 |$;R;;R;R;;Q;;;;;;;;;;_AP;;;;;;;;;;$,Sg:n:1:p:ht,Sg:n:3:q:ht,m:0:7.8.9.10.11.19,m:2:20.21.22.23.24.25 | *C.*C.*C1=CC=C(C2=C3C(=CC=C2)C(*)(*)C2=C3C=CC=C2)C=C1 |$R;;R;;_AP;;;;;;;;;;;;R;Q;;;;;;;;$,,,Sg:n:0:p:ht:::,Sg:n:2:q:ht:::,| | C<r>R</r>.C<r>R</r>.<r>R</r>C1(<r>Q</r>)C2=CC=CC(C3=CC=C(<r>_AP</r>)C=C3)=C2C2=C1C=CC=C2|Sg:n:1:p:ht,Sg:n:3:q:ht,m:0:7.8.9.10.11.19,m:2:20.21.22.23.24.25 | [
{
"bbox": [
0.3994140625,
0.0439453125,
0.439453125,
0.09765625
],
"text": "Q"
},
{
"bbox": [
0.5341796875,
0.0439453125,
0.5732421875,
0.0830078125
],
"text": "R"
},
{
"bbox": [
0.111328125,
0.0673828125,
0.205078... |
End of preview. Expand in Data Studio
MarkushGrapher 2 Datasets
Datasets for training and evaluating MarkushGrapher 2, a model for converting patent Markush structure images into CXSMILES representations.
Dataset Subsets
| Subset | Train | Test | Description | OCR |
|---|---|---|---|---|
uspto-mol-m-54k-new |
54,785 | 200 | USPTO-MOL-M Markush samples | ChemicalOCR predictions |
uspto-markush |
— | 74 | USPTO Markush structures benchmark | Ground Truth OCR |
m2s |
— | 103 | Mol2Smiles (M2S) benchmark | Ground Truth OCR |
IP5-markush |
— | 878 | IP5 Markush structures benchmark | Ground Truth OCR |
Features
Each sample contains:
page_image— Input patent image (PIL Image, typically 1024×1024)cells— OCR-detected text cells with bounding boxes (bboxin normalized coordinates,text)cxsmiles— Ground truth CXSMILES representationcxsmiles_opt— Optimized (tokenizer-friendly) CXSMILES representationcxsmiles_dataset— Original CXSMILES from the source datasetannotation— Annotation metadata (used to train model)image_name— Source image filenameid— Sample identifier
Usage
from datasets import load_dataset
# Load a specific subset
dataset = load_dataset("docling-project/MarkushGrapher-2-Datasets", "uspto-mol-m-54k")
# Load a benchmark subset
benchmark = load_dataset("docling-project/MarkushGrapher-2-Datasets", "m2s")
Note
MarkushGrapher-2 is also trained on the following datasets:
Phase 1: 243k real-world image–SMILES pairs from MolScribe
Phase 2:
- 235k synthetically generated image–CXSMILES pairs from MarkushGrapher-Datasets (v1)
- 91k samples from MolParser Dataset
Citation
If you use this dataset, please cite:
@inproceedings{strohmeyer2026markushgrapher2,
title = {MarkushGrapher-2: End-to-end Multimodal Recognition of Chemical Structures},
author = {Strohmeyer, Tim and Morin, Lucas and Meijer, Gerhard Ingmar and Weber, Valery and Nassar, Ahmed and Staar, Peter W. J.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2026}
}
License
This Dataset is released under the Creative Commons Attribution 4.0 License.
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Number of rows:
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Models trained or fine-tuned on docling-project/MarkushGrapher-2-Datasets