# Mini Model Developed for Text Extraction from White Backgrounds DONUT-inspired project offers a simple solution for text extraction tasks. - **Source:** [r/MachineLearning](https://www.reddit.com/r/MachineLearning) - **Published:** 2026-07-27 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/4848fe76-27be-44d3-aebb-feb3b4900a7e ## Project Origin Inspired by the DONUT academic paper, the developer initially aimed to extract product information from receipts. Due to complexity, they pivoted to a simpler white-background text extraction model, showcasing the MVP approach in machine learning. ## Technical Infrastructure Using VQVAET5 architecture, the model can identify text in visual documents. It performs particularly well on high-contrast (black text on white background) scenarios. The open-source code is available on GitHub for enthusiasts. ## Use Cases While currently handling basic text extraction, the model could form foundations for invoice data collection, handwriting recognition, and similar fields. Community feedback may enable adaptation to more complex scenarios. ## Current Limitations The project is in early stages with limited performance on complex backgrounds, low-resolution images, or handwritten text. It also doesn't yet achieve the original goal of receipt analysis. ## Community Contribution The developer indicates openness to open-source contributions through GitHub sharing, particularly expecting collaborations on dataset diversity and model optimization. ## Future Potential Such mini-projects demonstrate how practical AI solutions can be implemented simply. If the developer returns to the original receipt analysis goal, it could benefit the retail sector significantly. ## Sources covering this story - https://www.reddit.com/r/MachineLearning/comments/1v811sc/made_a_small_model_that_extracts_text_from_a/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._