How to Easily Fine-Tune the Donut Model for Receipt Information Extraction
Last Updated on November 11, 2025 by Editorial Team
Author(s): Eivind Kjosbakken
Originally published on Towards AI.
How to Easily Fine-Tune the Donut Model for Receipt Information Extraction
The Donut model in Python is a model to extract text from a given image. This can be useful in several scenarios, for example, when it comes to scanning receipts. You can easily download the Donut model from GitHub, but as is common with AI models, you should fine-tune the model for your specific needs. This article was written as I did not find any tutorials showing me exactly how to fine-tune the Donut model with my dataset. I therefore had to learn this from other tutorials (which will be linked throughout the tutorial) and figure out issues myself. These issues were especially prevalent as I did not have a GPU on my local computer, and to simplify the process for others, I therefore made this tutorial.
This article provides a comprehensive guide on fine-tuning the Donut model for receipt information extraction, detailing the necessary steps such as finding or creating a suitable dataset, utilizing Google Colab for the fine-tuning process, changing important parameters, and executing the fine-tuning process either locally or in a cloud environment. It also emphasizes the significance of proper configuration and outlines common issues related to package versions that users may encounter, thus ensuring a smoother implementation for data extraction tasks.
Read the full blog for free on Medium.
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