Ollama-OCR for High-Precision OCR with Ollama

Llama 3.2-Vision is a multimodal large language model available in 11B and 90B sizes, capable of processing both text and image inputs to generate text outputs. The model excels in visual recognition, image reasoning, image description, and answering image-related questions, outperforming existing open-source and closed-source multimodal models across multiple industry benchmarks.

Llama 3.2-Vision Examples

Handwriting

In this article I will describe how to call the Llama 3.2-Vision 11B modeling service run by Ollama and implement image text recognition (OCR) functionality using

Output：

The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of instruction-tuned image reasoning generative models in 118 and 908 sizes (text + images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks.

2. Markdown Output

import { ollamaOCR, DEFAULT_MARKDOWN_SYSTEM_PROMPT } from "ollama-ocr";



async function runOCR() {

  const text = await ollamaOCR({

    filePath: "./trader-joes-receipt.jpg",

    systemPrompt: DEFAULT_MARKDOWN_SYSTEM_PROMPT,

  });

  console.log(text);

}

Input Image：

ollama-ocr is using a local vision model, if you want to use the online Llama 3.2-Vision model, try the llama-ocr library.