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Multilingual Invoice Parsing project with LLaMA 4, OCR, and Python
Latest   Machine Learning

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

Last Updated on April 14, 2025 by Editorial Team

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 β€” 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 β€” 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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