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DECIMER—hand-drawn molecule images dataset


The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER — Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.

Graphical Abstract


Most chemical information is published in text and images in the primary scientific literature. The automated conversion of these unstructured, human-readable data formats into structured, machine-readable representations is essential to make the information available in publicly accessible databases. The reliable extraction of information from the depictions of the chemical structures is an ongoing challenge that still has not been fully solved yet. Chemical structure depictions are converted into computer-readable representations using optical chemical structure recognition (OCSR) systems [1].

The field of OCSR has developed significantly over the last 30 years. Most OCSR tools follow a hard-coded set of rules to assemble the underlying molecule based on the elements in the vectorised image [2,3,4,5,6,7,8,9,10,11]. By 2020 several deep learning-based solutions are available [12,13,14,15,16,17,18].

In order to evaluate the performance of the available OCSR tools, realistic benchmark datasets are necessary. At present, there are four real-world datasets available [1, 9, 19] that contain chemical structure depictions that were collected and curated from publications and patents. The evaluation of the performance on realistic data is crucial to demonstrate whether the tools are robust enough to be used in an automated chemical literature mining process.

The resolution of hand-drawn chemical structures is a more challenging task than the resolution of automatically generated depictions. In addition to the varying depiction features which are present anyway, the individual, unique way of drawing the structure adds an increased level of complexity. In 2021, the deep learning-based OCSR tool ChemPix [15] demonstrated its capability to interpret simple hand-drawn hydrocarbon structures with high accuracy. There also are a few closed-source methods and commercial systems available that claim to be capable of resolving hand-drawn chemical structures [20,21,22]. The authors of the deep-learning-based OCSR tool img2mol demonstrated the capability of their tool to recognise some hand-drawn chemical structures that they had picked themselves and noted the lack of a standardised benchmark set [14].

With the development of more OCSR tools that focus on the resolution of hand-drawn chemical structure depictions, there is a need for a standardised dataset to evaluate their performance. Here we present DECIMER — Hand-drawn molecule images, a set of 5088 hand-drawn chemical structures depictions. Every image is mapped to a machine-readable representation of the underlying molecule. The diversely picked molecules represent a wide variety of small molecules. The dataset was created to facilitate the ongoing development in the field of OCSR and is openly accessible.

Data description

The dataset consists of 5088 PNG images of unique hand-drawn chemical structure depictions (Fig. 1) which are mapped to their corresponding SMILES [23] string as well as an SD file. The structures have been drawn by 24 volunteers from the Westphalian University of Applied Sciences, Campus Recklinghausen, Germany, who have graciously offered to use their free time to contribute to the generation of this dataset.

Fig. 1
figure 1

Examples of hand-drawn chemical structure depictions from the dataset

The molecules have been picked from all structures in PubChem [24] using RDKit’s implementation of the MaxMin algorithm [25] based on Morgan fingerprints [26] to ensure a diverse coverage of the chemical space. The only filtering rule that has been applied is a molecular weight maximum of 1500 Da. As a consequence, features like stereochemical information, charged groups as well as different types of isotopes are present in the dataset.

There are two categories of images:

  • Drawn on a piece of white paper and scanned (Fig. 2)

  • Drawn using a mobile device or tablet and directly saved as an image (Fig. 3).

Fig. 2
figure 2

A chemical structure depiction generated by CDK, sketched on a sheet of paper and scanned as an image file

Fig. 3
figure 3

A chemical structure depiction generated by CDK, sketched on a tablet and saved as an image file


In total, 6000 diverse molecules were selected from PubChem using RDKit’s implementation of the MaxMin algorithm based on Morgan fingerprints. Subsequently, CDK Depict [27], a structure depiction generator based on the Chemistry Development Kit (CDK) [28], was used to create production-quality 2D images in batches. Each batch of images was then converted into PDF files and they were distributed among the volunteers. Using the chemical structure depictions generated by CDK as a visual template, each volunteer drew the structures on a piece of paper using a black or blue pen or on their tablet using an input device.

Each volunteer sent back the scanned images or the images generated using their device after completing a batch. The curators reviewed the drawings, manually confirmed the correctness of the molecules, cropped the scanned images and stored them in separate image files. As part of the curation, structures that weren't correct due to human error were discarded. A total of 568 images out of 6000 were rejected due to issues with the depicted structure. Another 344 structures were not returned by the volunteers. This resulted in the final dataset of 5088 images in total.

An identifier was assigned to each image, and the same identifier was used to label the SD file which was generated using the CDK. Additionally, the dataset contains a file containing a table of the identifiers and corresponding SMILES representations.


The following steps were taken in order to make the dataset findable, accessible, interoperable and reusable (FAIR) [29]. The dataset was deposited in a publicly accessible data repository, in this case, Zenodo. This ensures that the dataset is easily findable. Furthermore, Zenodo provides a digital object identifier (DOI) that can be used to locate the dataset and it can also easily be integrated into Github as well. With Zenodo being an open, public repository, the dataset can be accessed from any part of the globe. To make it as interoperable as possible, the generated images use PNG as the final image format, which can be used across a variety of operating systems. Additionally, SMILES and SDF are representations of chemical structures which can be read by every cheminformatics toolkit. The dataset has been published under the CC-BY 4.0 licence. This licence includes that every user can redistribute or change the data as much as they want as long as they refer to the original authors when publishing results based on it. It is possible to use the data for non-commercial or commercial purposes without further obligations.


No restrictions or limitations apply to using and reusing the dataset. Everyone can use this dataset as a standard benchmark set for the evaluation of the performance of their OCSR tools. The dataset includes a wide range of chemical structures and represents a much larger chemical space. The structures were drawn by various individuals to ensure the diversity of drawing styles. The main limitation is caused by the molecular weight filter (< 1500Da) as it excludes certain molecules like big macrocycles, proteins or artificial polymers. Additionally, Markush structures are not represented.

Due to the limited number of images in this dataset, we do not recommend attempting to train a deep learning model using this dataset. We highly recommended using it exclusively for benchmarking instead of fitting the tools to the dataset.

Availability of data and materials

The dataset is openly available at ZENODO:



Chemistry development kit


Creative commons


Digital object identifier


Findable, accessible, interoperable, and reusable


Optical chemical structure recognition


Portable document format


Portable network graphics


Structural data file


Structure diagram generator


Simplified molecular-input line-entry system


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The authors would like to acknowledge the following students of the Westphalian University of Applied Sciences for their contribution to the project: Muammer Ates, Samuel Behr, Thilo Bredtmann, Michele Cassano, Saaruky Chanthirakanthan, Zeynep Dagtekin, Dustin Finke, Janina Gleißenberger, Safiye Sarah Kantar, Irina Krützner, Lilly Maddox, Fabian Matten, Timo van Meegdenburg, Alisa Muminovic, Paulina Paszko, Josefine Reiser, Maximilian Rottmann, Lisa Scharfenberg, Patricia Schoof, Betül Sevindik, Fatma Zehra Sevindik, Kaan Sipahi and Julia Zielinski.


Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Carl-Zeiss-Foundation and by the German Research Foundation within the framework CRC1127 ChemBioSys.

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KR and HOB initiated the project and curated the final dataset. CS and AZ conceived the project and supervised the work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kohulan Rajan.

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Competing interests

AZ is co-founder of GNWI—Gesellschaft für naturwissenschaftliche Informatik mbH, Dortmund, Germany.

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Brinkhaus, H.O., Zielesny, A., Steinbeck, C. et al. DECIMER—hand-drawn molecule images dataset. J Cheminform 14, 36 (2022).

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