Hugging Face LayoutLMv2 Model True Inference

I explain why OCR quality matters for Hugging Face LayoutLMv2 model performance, related to document data classification. If input from OCR is poor, ML classification inference results will be low quality too. This is why it is important to use high quality OCR system to extract text and coordinates from the document, before applying ML solution.

 

Get Receipt Data with Hugging Face ML Model

This tutorial is about how to use fine-tuned Hugging Face model to extract data from scanned receipt documents. We are executing inference action - passing receipt image, along with words and coordinates to the model. As a result, we get back predictions - class labels assigned to each input. This helps to classify document elements and extract correct data. I share a hint on how to match input words with classified labels. Input words and coordinates are expected to be retrieved from separate OCR.

 

Fine-Tuning with Hugging Face Trainer

In this tutorial, I explain how I was using Hugging Face Trainer with PyTorch to fine-tune LayoutLMv2 model for data extraction from the documents (based on CORD dataset with receipts). The advantage of Hugging Face Trainer - it simplifies model fine-tuning pipeline and you can easily upload the model to Hugging Face model hub.

 

Hugging Face Datasets - Example with Receipts Data

Hugging Face Datasets library provides a useful API to work with data for ML model fine tuning. It allows you to load and process any external datasets with your own Python functions. As a result, you will get a unified data interface and could reuse the same API for fine-tuning various Hugging Face models.