image-ocr

image-ocr is a skill for Claude Code, Codex from xuansenpa1/skillrevise. It costs 16 tokens per session (2,633 once invoked), scanned A, a copy of image-ocr, MIT.

A text-recognition tool that reads words from image files using Tesseract OCR. OCR means converting text in pictures into machine-readable text.

In plain words
What is it for?
Use it to extract raw text or structured information from images, including forms, receipts, and tables.
Why use it?
It removes the need to retype content from scans, screenshots, photos, receipts, and forms.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to extract raw text or structured information from images, including forms, receipts, and tables.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xuansenpa1/skillrevise/image-ocr
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add xuansenpa1/skillrevise --skill image-ocr
Clone the repo
git clone --depth 1 https://github.com/xuansenpa1/skillrevise

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for image-ocr

README.md
[![agentmods](https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/image-ocr.svg)](https://agentmods.dev/skills/xuansenpa1/skillrevise/image-ocr)
Your own site
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/image-ocr"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/image-ocr.svg" alt="Measured on agentmods" height="20"></a>
Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,633 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00016 $0.02633
Opus 5 $0.00008 $0.01316
Sonnet 5 $0.00003 $0.00527
Haiku 4.5 $0.00002 $0.00263

Measured 4d ago against content hash 69dd1065075d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

image-ocr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to image-ocr — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

data/skillsbench/tasks/jpg-ocr-stat/environment/skills/image-ocr/SKILL.md · 393 lines

How it starts

The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Image OCR Skill

Purpose

This skill enables accurate text extraction from image files (JPG, PNG, etc.) using Tesseract OCR via the pytesseract Python library. It is suitable for scanned documents, screenshots, photos of text, receipts, forms, and other visual content containing text.

When to Use

  • Extracting text from scanned documents or photos
  • Reading text from screenshots or image captures
  • Processing batch image files that contain textual information
  • Converting visual documents to machine-readable text
  • Extracting structured data from forms, receipts, or tables in images

Required Libraries

The following Python libraries are required:

import pytesseract
from PIL import Image
import json
import os

Input Requirements

  • File formats: JPG, JPEG, PNG, WEBP
  • Image quality: Minimum 300 DPI recommended for printed text; clear and legible text
  • File size: Under 5MB per image (resize if necessary)
  • Text language: Specify if non-English to improve accuracy

Output Schema

All extracted content must be returned as valid JSON conforming to this schema:

{
  "success": true,
  "filename": "example.jpg",
  "extracted_text": "Full raw text extracted from the image...",
  "confidence": "high|medium|low",
  "metadata": {
    "language_detected": "en",
    "text_regions": 3,
    "has_tables": false,
    "has_handwriting": false
  },
  "warnings": [
    "Text partially obscured in bottom-right corner",
    "Low contrast detected in header section"
  ]
}

Field Descriptions

  • success: Boolean indicating whether text extraction completed
  • filename: Original image filename
  • extracted_text: Complete text content in reading order (top-to-bottom, left-to-right)
  • confidence: Overall OCR confidence level based on image quality and text clarity
  • metadata.language_detected: ISO 639-1 language code
  • metadata.text_regions: Number of distinct text blocks identified
  • metadata.has_tables: Whether tabular data structures were detected
  • metadata.has_handwriting: Whether handwritten text was detected
  • warnings: Array of quality issues or potential errors

Read the full file on GitHub · 393 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 393 lines · 16 tokens per session scan A 69dd1065075d

Subscribe to this mod's changes

image-ocr is a skill published in the GitHub repository xuansenpa1/skillrevise (55 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 2,633 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to image-ocr, differing in 0 lines, and is treated as a copy.

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