DECIMER.ai (Deep Learning for Chemical Image Recognition - WebApp)

This repository contains the code running on decimer.ai

Deep Learning for Chemical Image Recognition (DECIMER) is a step towards automated chemical image segmentation and recognition. DECIMER is actively developed and maintained by the Steinbeck group at the Friedrich Schiller University Jena.

How to run DECIMER Web locally

git clone https://github.com/OBrink/DECIMER.ai.git
sudo chmod -R 777 DECIMER.ai
cd DECIMER.ai/
mv .env.example .env
sed -i '$ d' routes/web.php (Which deletes the last line "URL::forceScheme('https');")
sudo chmod -R 777 storage/
sudo chmod -R 777 bootstrap/cache/
docker-compose up --build -d

• Open your browser (DECIMER works best on Chrome and Chromium-based web browsers) and enter http://localhost:80
• On the first run, you will be asked to generate an app key for the Laravel app
• Click on "Generate app key"
• Refresh the webpage. Now, DECIMER.ai is running locally on your machine. Have fun!
• It may take 5-10 minutes until all models are loaded and the app can be run without errors.

Check out the DECIMER.ai wiki!

• Instructions on how to set up a smaller version of DECIMER.ai - Currently, the default version in this repository consumes approximately 20 GB of memory. This can be scaled down drastically (at the cost of parallel processing speed).
• Instructions on how to remove the limitation to 10 pages and 20 structures in your locally running version of DECIMER Web
• https://github.com/OBrink/DECIMER.ai/wiki

DECIMER.AI is powered by

License:

• This project is licensed under the MIT License - see the LICENSE file for details

Citation

Please cite work if you use it:

Rajan K, Brinkhaus HO, Agea MI, Zielesny A, Steinbeck C (2023) DECIMER.ai - An open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications. 
ChemRxiv. doi: 10.26434/chemrxiv-2023-xhcx9 This content is a preprint and has not been peer-reviewed.

Further reading

• DECIMER: towards deep learning for chemical image recognition: Rajan, K., Zielesny, A., Steinbeck, C. J Cheminform, 12, 65 (2020).
• DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature: Rajan, K., Brinkhaus, H.O., Sorokina, M. et al. J Cheminform, 13, 20 (2021).
• DECIMER 1.0: deep learning for chemical image recognition using transformers: Rajan, K., Zielesny, A., Steinbeck, C. J Cheminform, 13, 61 (2021).
• STOUT: SMILES to IUPAC names using neural machine translation: Rajan, K., Zielesny, A., Steinbeck, C. J Cheminform, 13, 34 (2021).

Research Group