How do I install the Image Ocr skill?

Run npx skillmds@latest add varunr89/image-ocr in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Image Ocr safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Image Ocr?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Image Ocr free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Image Ocr?

varunr89 (@varunr89) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Image Ocr by varunr89 — Agent Skill

Name: Image Ocr
Author: varunr89

File contents

Image OCR

Batch-processes a directory of JPG/JPEG images and produces a combined Markdown file containing the OCR text from each image.

When to use

Use this skill when the user wants to extract text from one or more JPG/JPEG images -- for example scanned documents, photos of whiteboards, or screenshots.

Backends

vision (default on macOS/Apple Silicon) -- Uses Apple's Vision framework via PyObjC. Fast, on-device, GPU-accelerated. Requires pyobjc-core and pyobjc-framework-Vision.
tesseract -- Uses the tesseract CLI binary. Cross-platform. Optional preprocessing (grayscale, autocontrast, upscale) via Pillow.
mlx_ocr -- Uses MLX-based OCR on Apple Silicon. Runs in a subprocess to isolate potential Metal crashes. Requires Python 3.10+ and mlx-ocr.
auto -- Tries vision first, falls back to tesseract.

Usage

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/ocr_jpgs_to_markdown.py \
  --input <directory-of-jpgs> \
  --output <output.md> \
  [--backend auto|vision|tesseract|mlx_ocr] \
  [--start N] [--limit N] \
  [--languages en-US] \
  [--fast] \
  [--workers N] \
  [--tesseract-lang eng] [--tesseract-psm 6] [--tesseract-oem 1] \
  [--preprocess] [--preprocess-scale 2.0] \
  [--det-lang eng] [--rec-lang lat] \
  [--mlx-worker-python /path/to/python3.11]

Key arguments

Argument	Default	Description
`--input`	`ezgif-...`	Directory containing .jpg/.jpeg files
`--output`	`training_plan_ocr.md`	Output Markdown file path
`--backend`	`auto`	OCR backend: auto, vision, tesseract, mlx_ocr
`--start`	0	Start index (0-based) within sorted file list
`--limit`	None	Max files to process
`--languages`	`en-US`	Comma-separated recognition languages (Vision)
`--fast`	false	Use faster, less accurate recognition level (Vision)
`--workers`	CPU count	Number of parallel workers
`--preprocess`	false	Grayscale/autocontrast/upscale before tesseract
`--preprocess-scale`	2.0	Upscale factor for preprocessing

Dependencies

macOS + PyObjC for Vision backend (pip install pyobjc-core pyobjc-framework-Vision)
tesseract binary for tesseract backend (brew install tesseract)
mlx-ocr + Python 3.10+ for MLX backend
Pillow for preprocessing and MLX

---name: image-ocrdescription: Extract text from a directory of JPG/JPEG images into a single Markdown file using Apple Vision, tesseract, or MLX OCR---# Image OCRBatch-processes a directory of JPG/JPEG images and produces a combined Markdown file containing the OCR text from each image.## When to useUse this skill when the user wants to extract text from one or more JPG/JPEG images -- for example scanned documents, photos of whiteboards, or screenshots.## Backends- **vision** (default on macOS/Apple Silicon) -- Uses Apple's Vision framework via PyObjC. Fast, on-device, GPU-accelerated. Requires `pyobjc-core` and `pyobjc-framework-Vision`.- **tesseract** -- Uses the `tesseract` CLI binary. Cross-platform. Optional preprocessing (grayscale, autocontrast, upscale) via Pillow.- **mlx_ocr** -- Uses MLX-based OCR on Apple Silicon. Runs in a subprocess to isolate potential Metal crashes. Requires Python 3.10+ and `mlx-ocr`.- **auto** -- Tries vision first, falls back to tesseract.## Usage```bashpython3 ${CLAUDE_PLUGIN_ROOT}/scripts/ocr_jpgs_to_markdown.py \ --input <directory-of-jpgs> \ --output <output.md> \ [--backend auto|vision|tesseract|mlx_ocr] \ [--start N] [--limit N] \ [--languages en-US] \ [--fast] \ [--workers N] \ [--tesseract-lang eng] [--tesseract-psm 6] [--tesseract-oem 1] \ [--preprocess] [--preprocess-scale 2.0] \ [--det-lang eng] [--rec-lang lat] \ [--mlx-worker-python /path/to/python3.11]```## Key arguments| Argument | Default | Description ||---|---|---|| `--input` | `ezgif-...` | Directory containing .jpg/.jpeg files || `--output` | `training_plan_ocr.md` | Output Markdown file path || `--backend` | `auto` | OCR backend: auto, vision, tesseract, mlx_ocr || `--start` | 0 | Start index (0-based) within sorted file list || `--limit` | None | Max files to process || `--languages` | `en-US` | Comma-separated recognition languages (Vision) || `--fast` | false | Use faster, less accurate recognition level (Vision) || `--workers` | CPU count | Number of parallel workers || `--preprocess` | false | Grayscale/autocontrast/upscale before tesseract || `--preprocess-scale` | 2.0 | Upscale factor for preprocessing |## Dependencies- macOS + PyObjC for Vision backend (pip install pyobjc-core pyobjc-framework-Vision)- tesseract binary for tesseract backend (brew install tesseract)- mlx-ocr + Python 3.10+ for MLX backend- Pillow for preprocessing and MLX

Frequently asked questions

Image Ocr