RxnCaption-11k Dataset

Dataset Overview

RxnCaption-11k Dataset is a large-scale dataset specifically designed for chemical reaction diagram parsing, containing chemical reaction images extracted from scientific literature PDFs along with detailed annotations. This dataset aims to support research in cheminformatics, document analysis, and computer vision fields.

Dataset Statistics

• Total Images: 10,512 images
  • Train set: 10,112 images
  • Test set: 400 images
• Total Reactions: 27,545 reactions
  • Train set: 24,716 reactions
  • Test set: 2,829 reactions
• Data Source: Scientific literature PDF files
• Image Format: PNG
• Total Annotations: Approximately 79,167 annotation instances

Dataset Structure

rxncaption-11k/
├── train_set/
│   ├── ground_truth.json          # Train set annotation file
│   └── images/                    # Train set image directory
└── test_set/
    ├── ground_truth.json          # Test set annotation file
    └── images/                    # Test set image directory

Annotation Category Definitions

The dataset defines 4 main categories, each containing multiple fine-grained attributes:

1. Structure - category_id: 1

• P-reactants: Reactant molecular structures
• P-product: Product molecular structures
• P-reaction conditions: Reaction condition molecular structures

2. Text - category_id: 2

• T-reaction conditions: Reaction condition text
• T-reactants: Reactant text
• T-product: Product text

3. Identifier - category_id: 3

• Chemical identifiers and numbers

4. Supplement - category_id: 4

• Other supplementary information

Annotation Statistics

Attribute Type	Annotation Count	Percentage
T-reaction conditions	34,947	44.1%
P-reactants	18,420	23.3%
P-product	17,975	22.7%
T-reactants	3,422	4.3%
T-product	1,776	2.2%
P-reaction conditions	1,627	2.1%

Data Format

Image File Naming

Image filenames are hashed (SHA-256, first 8 hex chars). Example: a1b2c3d4.png.

Annotation File Format (ground_truth.json)

The annotation file follows COCO format and contains the following main fields:

{
  "licenses": [...],
  "info": {
    "description": "A dataset for chemical visual diagram analysis",
    "version": "v1",
    "year": "2025"
  },
  "categories": [
    {"id": 1, "name": "structure"},
    {"id": 2, "name": "text"},
    {"id": 3, "name": "identifier"},
    {"id": 4, "name": "supplement"}
  ],
  "images": [
    {
      "id": 2,
      "class": "figure",
      "confidence": 0.9148465991020203,
      "bbox": [x1, y1, x2, y2],
      "original_id": 0,
      "width": 1008.7104797363281,
      "height": 471.88232421875,
      "file_name": "ays765k9.png",
      "bboxes": [
        {
          "id": 0,
          "bbox": [x, y, width, height],
          "category_id": 1,
          "category": "P",
          "attribute": "P-reactants",
          "region_id": ["akzkPsql"]
        }
      ]
    }
  ]
}

Annotation Field Descriptions

• id: Unique annotation identifier
• bbox: Bounding box coordinates [x1, y1, x2, y2] for image-level bbox, [x, y, width, height] for bboxes list
• category_id: Category ID (1-4)
• category: Category abbreviation (P=Structure, T=Text)
• attribute: Specific attribute name
• region_id: List of region identifiers

Use Cases

This dataset is suitable for the following research tasks:

1. Chemical Structure Recognition: Identify and locate molecular structures in chemical reactions
2. Text Information Extraction: Extract text information from chemical diagrams
3. Reaction Condition Analysis: Identify and analyze reaction conditions
4. Document Understanding: Understand chemical information in scientific literature
5. Multimodal Learning: Combine visual and text information for chemical analysis

Data Quality

• All images are sourced from high-quality scientific literature
• Annotations are professionally verified for accuracy
• Contains samples of various chemical reaction types and complexities
• Supports fine-grained chemical information analysis

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Terms of Use

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
• NonCommercial — You may not use the material for commercial purposes. Commercial use is prohibited without explicit permission from the licensor.

Contact

For questions or suggestions, please contact [email protected]

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Last updated: 2025