Ollama OCR

Use Ollama's vision/OCR models to recognize text from images. Supports glm-ocr, llava, moondream, and llama3.2-vision models. Ideal when you need local offline OCR without relying on cloud APIs.

Ollama OCR Skill

Use this skill when you need to recognize text from images using Ollama's local vision/OCR models. No internet required - fully offline OCR.

When to Use

• User sends an image and wants text extraction
• User asks to recognize text from a screenshot or picture
• Need local offline OCR without cloud API dependency
• Processing sensitive images that shouldn't be sent to third parties

Models Available

Model	Best For	Size
glm-ocr:latest	Chinese text OCR	~2.2GB
llava:7b	General image understanding	~4.7GB
moondream	Lightweight vision model	~1.5GB
llama3.2-vision:latest	Large vision model	~7GB+

Ollama Endpoint

Default: http://172.17.0.2:11434 (Docker container to host gateway)

Note: Endpoint is pre-configured for OpenClaw running in Docker accessing host Ollama. Adjust OLLAMA_HOST in ollama_ocr.py if your setup differs.

Usage

Command Line

python3 ollama_ocr.py /path/to/image.jpg [model_name]

Examples:

python3 ollama_ocr.py receipt.png glm-ocr:latest
python3 ollama_ocr.py screenshot.jpg llava:7b

Python API

from ollama_ocr import ollama_ocr

# Basic OCR with default model (glm-ocr)
result = ollama_ocr('/path/to/image.jpg')

# Specify model
result = ollama_ocr('/path/to/image.jpg', 'glm-ocr:latest')

print(result)

Example Prompts to Activate This Skill

• "识别这张图片里的文字"
• "帮我 OCR 一下这个截图"
• "Extract text from this image"
• "What text is in this screenshot?"

Notes

• Image path must be absolute or relative to script location
• For large images, consider resizing first to avoid timeout
• glm-ocr works best for Chinese text
• Some models may have output quirks (e.g., glm-ocr occasionally repeats)
• First call may be slow if model isn't cached in memory

Requirements

• Ollama installed and running
• At least one vision/OCR model downloaded (e.g., ollama pull glm-ocr:latest)