The tutorial demonstrates how to rapidly build an optical character recognition (OCR) application capable of extracting structured data from images using artificial intelligence on your own machine. By leveraging Ollama, a tool designed to run open-source large language models such as Llama and Mistral locally, developers can avoid costly external APIs and operate fully offline. The article walks through setting up Ollama with a specialized vision model, llama3.2-vision, highlighting that hardware like Apple Silicon Macs, Nvidia or AMD GPUs, or TPUs provide an optimal environment for these tasks.
With the goal of extracting information from invoices, readers are guided through a practical example involving a simple Node.js script. The approach uses Zod, a schema validation library, to define an expected data structure—such as client name and invoice amounts—ensuring that only relevant information is parsed by the artificial intelligence model. The script, built for Node.js 20, includes package installations for needed dependencies (ollama, zod, zod-to-json-schema), pulls the appropriate vision model, and crafts a format using the Zod schema to request and validate structured output from the artificial intelligence system.
The demonstration processes a plain invoice image, not a text-based PDF, showcasing the model´s ability to extract fields like customer name, amount excluding tax, and total amount including tax with high accuracy and speed. Testing validated solid performance on both Apple M1 Max systems and systems with Nvidia RTX 2080 Ti GPUs, provided the model (~8GB) fits into memory. The final results are clean JSON outputs extracted in seconds from images—a workflow that previously required extensive engineering and commercial OCR systems. The author concludes by observing that artificial intelligence is radically reducing technical barriers, enabling individuals to build advanced, business-grade automation with minimal effort and local resources.
