python-ocr-expertise — for Claude Code python-ocr-expertise, awesome-list-site, community, for Claude Code, ide skills, awesome, awesome-list, awesome-lists, awesomeness, shadcn-ui

v1.0.0

Об этом навыке

Подходящий сценарий: Ideal for AI agents that need python ocr expertise. Локализованное описание: Transform any GitHub awesome list into a sophisticated, interactive web dashboard with AI-powered enhancements, advanced search, and modern UI components. It covers awesome, awesome-list, awesome-lists workflows. This AI agent skill supports Claude Code, Cursor, and

Возможности

Python OCR Expertise
APPLICABILITY GUARD
Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.
Extracting text from images or scanned documents
Building production OCR pipelines

# Core Topics

krzemienski krzemienski
[1]
[1]
Updated: 3/23/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
64
Canonical Locale
en
Detected Body Locale
en

Подходящий сценарий: Ideal for AI agents that need python ocr expertise. Локализованное описание: Transform any GitHub awesome list into a sophisticated, interactive web dashboard with AI-powered enhancements, advanced search, and modern UI components. It covers awesome, awesome-list, awesome-lists workflows. This AI agent skill supports Claude Code, Cursor, and

Зачем использовать этот навык

Рекомендация: python-ocr-expertise helps agents python ocr expertise. Transform any GitHub awesome list into a sophisticated, interactive web dashboard with AI-powered enhancements, advanced search, and modern UI

Подходит лучше всего

Подходящий сценарий: Ideal for AI agents that need python ocr expertise.

Реализуемые кейсы использования for python-ocr-expertise

Сценарий использования: Applying Python OCR Expertise
Сценарий использования: Applying APPLICABILITY GUARD
Сценарий использования: Applying Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines

! Безопасность и ограничения

  • Ограничение: Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.
  • Ограничение: Only activate for Python OCR libraries or Python-based text extraction pipelines
  • Ограничение: Requires repository-specific context from the skill documentation

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is python-ocr-expertise?

Подходящий сценарий: Ideal for AI agents that need python ocr expertise. Локализованное описание: Transform any GitHub awesome list into a sophisticated, interactive web dashboard with AI-powered enhancements, advanced search, and modern UI components. It covers awesome, awesome-list, awesome-lists workflows. This AI agent skill supports Claude Code, Cursor, and

How do I install python-ocr-expertise?

Run the command: npx killer-skills add krzemienski/awesome-list-site/python-ocr-expertise. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for python-ocr-expertise?

Key use cases include: Сценарий использования: Applying Python OCR Expertise, Сценарий использования: Applying APPLICABILITY GUARD, Сценарий использования: Applying Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.

Which IDEs are compatible with python-ocr-expertise?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for python-ocr-expertise?

Ограничение: Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.. Ограничение: Only activate for Python OCR libraries or Python-based text extraction pipelines. Ограничение: Requires repository-specific context from the skill documentation.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add krzemienski/awesome-list-site/python-ocr-expertise. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use python-ocr-expertise immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

python-ocr-expertise

Install python-ocr-expertise, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Python OCR Expertise

APPLICABILITY GUARD

Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.

When to Use

  • Extracting text from images or scanned documents
  • Building production OCR pipelines
  • Choosing between OCR libraries
  • Debugging OCR accuracy issues

When NOT to Use

  • Document understanding beyond text extraction (use ai-multimodal)
  • PDF text extraction from digital PDFs (use pdf skill — no OCR needed)
  • Non-Python OCR implementations
  • Simple screenshot text reading (use Claude's vision directly)

Anti-Patterns

NEVERWHYFix
Skip image preprocessingOCR accuracy drops 30-50% on raw imagesAlways apply: grayscale → blur → Otsu binarization → deskew
Use default PSM mode for single-line textPSM 3 (auto) wastes time on layout analysis for simple inputsUse PSM 7 (single line) or PSM 8 (single word) for targeted extraction
Ignore confidence scoresLow-confidence results introduce garbage text silentlyFilter at 0.7 threshold minimum; adjust per use case
Use PyTesseract for multilingual productionTesseract accuracy lags significantly behind deep learning optionsUse PaddleOCR (best accuracy) or EasyOCR (easiest setup) for multilingual
Load OCR models per-request in web servicesModel loading takes 2-10 seconds; kills response timeInitialize model once at startup; reuse across requests

Conflicts

  • pdf skill: Use pdf skill for digital PDF text; this skill for scanned images/documents needing OCR.
  • ai-multimodal: Use ai-multimodal for document understanding; this skill for raw text extraction.

Library Selection

Use CaseLibraryWhy
Simple text extractionPyTesseractLightest setup, adequate for clean images
Best accuracy (multilingual)PaddleOCRState-of-the-art PP-OCRv4/v5
Quick setup, 80+ languagesEasyOCROne-liner setup, good accuracy
Document understandingdocTRLayout + text in one pipeline
Handwritten textTrOCRTransformer-based, best for handwriting
Edge/mobile deploymentRapidOCRONNX-based, lightweight

Accuracy hierarchy: PaddleOCR >= docTR > EasyOCR > PyTesseract

Essential Preprocessing

python
1import cv2 2def preprocess_for_ocr(image_path): 3 img = cv2.imread(image_path) 4 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 5 blur = cv2.GaussianBlur(gray, (5, 5), 0) 6 _, binary = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 7 return binary

PyTesseract PSM Modes (Expert Knowledge)

PSMUse Case
3Fully automatic (default)
4Single column, variable sizes
6Uniform block of text
7Single text line
8Single word
11Sparse text (find as much as possible)
13Raw line (bypass hacks)

Intake & Routing

TaskReference File
Build new OCR pipelineworkflows/build-ocr-pipeline.md
Choose/compare librariesworkflows/library-selection.md
Debug accuracy issuesreferences/troubleshooting.md
Image preprocessingreferences/preprocessing.md
PyTesseract deep divereferences/pytesseract.md
PaddleOCR deep divereferences/paddleocr.md
EasyOCR deep divereferences/easyocr.md
docTR/keras-ocr/TrOCRreferences/doctr.md
  • ai-multimodal — document understanding beyond OCR
  • pdf — PDF text extraction (digital, non-scanned)
  • media-processing — image preprocessing pipelines

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