---
title: "Document AI: Fine-tuning LayoutLM for document-understanding using Hugging Face Transformers"
url: https://daily.dev/posts/document-ai-fine-tuning-layoutlm-for-document-understanding-using-hugging-face-transformers-osiuunlzw
source_url: https://www.philschmid.de/fine-tuning-layoutlm
type: article
source: "Philipp Schmid"
published: 2026-05-31T07:41:00.564Z
updated: 2026-05-31T08:20:51.616Z
tags: ["deep-learning", "nlp"]
reading_time: 4
upvotes: 0
comments: 0
language: en
---

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# Document AI: Fine-tuning LayoutLM for document-understanding using Hugging Face Transformers

**[Philipp Schmid](https://daily.dev/sources/philschmid)** · 4 min read · 0 upvotes · 0 comments

## Summary

A step-by-step guide to fine-tuning LayoutLM (v1) for document understanding and information extraction using Hugging Face Transformers. The tutorial covers setting up the environment with pytesseract for OCR, loading and preprocessing the FUNSD dataset (199 annotated forms), training with the Hugging Face Trainer API, and running inference to extract text and bounding boxes from form images. The fine-tuned model achieves an F1 score of 0.787 using only 149 training examples, demonstrating the effectiveness of transfer learning for document AI tasks. LayoutLM v1 is highlighted as the only MIT-licensed model in the LayoutLM family, making it suitable for commercial use.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.philschmid.de/fine-tuning-layoutlm>

---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#nlp](https://daily.dev/tags/nlp)

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