Multilingual Invoice Parsing project with LLaMA 4, OCR, and Python

Author(s): Mouez Yazidi

Originally published on Towards AI.

Hi, I’m Mouez Yazidi, an AI & NLP Engineer. Today, I will explain how Artificial Intelligence and Large Language Models (LLMs) are revolutionizing OCR (Optical Character Recognition) tasks.

In this blog, I’ll walk you through a real-world use case — parsing invoices using Meta’s latest multimodal model, LLaMA 4. We’ll see how this cutting-edge technology not only improves text extraction but also enhances accuracy and understanding in document processing.

Let’s have quick taste of the final application, before we dive deeper into details:

🚀 Introduction to LLaMA 4: Understand what Meta’s LLaMA 4 is, its key strengths, and why it’s a game-changer in the multimodal AI space.📄 Real-World Use Case — Invoice Parsing: Learn how to apply LLaMA 4 to automate and enhance invoice parsing using its powerful multimodal capabilities.🧹 Structured Output with Pydantic: Discover how to refine and validate the model’s output using BaseModel from Pydantic for clean and structured data.🌍 Multilingual OCR Parsing: Test the robustness of your solution by parsing invoices in English, French, and Arabic, showcasing LLaMA 4’s multilingual understanding.🛠️ Building a Streamlit App: Step-by-step guide to building an interactive Streamlit app for invoice parsing, and deploying it to the cloud.

Meta has started a new chapter in artificial intelligence… Read the full blog for free on Medium.

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Published via Towards AI