Local Ocr Vlm

Local OCR VLM

Send image(s) to a locally hosted OpenAI-compatible vision endpoint and capture the text. Defaults to http://127.0.0.1:1234/v1. The tool is a single, zero-dependency Node script; no Python or SDK is required.

When to use

• OCR screenshots, scans, or document images on-machine.
• Batch a folder of images into per-image markdown files.
• Keep OCR offline using LM Studio or a vLLM/vmlx-compatible API.

Prerequisites

• Node.js 18+ (uses the built-in global fetch; no packages to install).
• A local OpenAI-compatible server exposing a /v1 API with a vision-capable model loaded. Qwen 3.5 9B is preferred; Qwen 3.5 27B also works.

Commands

Run the portable entry point:

bin/local-ocr-vlm invoice.png
bin/local-ocr-vlm --input-dir doc_input --output-dir doc_output --max-workers 4
bin/local-ocr-vlm --input-dir doc_input --model "qwen3.5-vl-9b"

Run bin/local-ocr-vlm --help for every flag. See [references/reference.md](references/reference.md) for the endpoints called, model auto-selection, thinking-mode handling, and exit codes.

Workflow

1. Confirm a local endpoint is serving /v1 with a vision model loaded.
2. Point the tool at one or more images and/or an --input-dir. With no --output-dir, results print to stdout; with one, each image becomes .md.
3. Let the tool auto-select a model, or pass --model when the endpoint hosts several.
4. Tune --max-workers for throughput; use 1 for strictly sequential processing.

Rules

• Never point the tool at a remote/hosted endpoint when the intent is to keep images on-machine; the default base URL is loopback.
• The tool reads images and writes only markdown output; it does not modify input images.
• Treat an unreachable endpoint or a per-image request failure as an explicit error. A run exits non-zero if any image fails.

Use [agents/ocr-operator.md](agents/ocr-operator.md) when a subagent should choose flags and verify the endpoint before running a batch.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: batteryshark
• Source: batteryshark/skill-tap
• License: MIT

Install and usage instructions live in the source repository linked above.