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
GitHub

关于此技能

适用场景： Ideal for AI agents that need python ocr expertise. 本地化技能摘要： 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 Windsurf workflows.

功能特性

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

# 核心主题

for Claude Code awesome awesome-list awesome-lists awesomeness shadcn-ui
krzemienski krzemienski
[1]
[1]
更新于: 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 teams, 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

适用场景： Ideal for AI agents that need python ocr expertise. 本地化技能摘要： 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 Windsurf workflows.

核心价值

推荐说明： 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 components.

适用 Agent 类型

适用场景： Ideal for AI agents that need python ocr expertise.

↓ 赋予的主要能力 · python-ocr-expertise

适用任务： Applying Python OCR Expertise
适用任务： Applying APPLICABILITY GUARD
适用任务： Applying Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines

! 使用限制与门槛

  • 限制说明： Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.
  • 限制说明： Only activate for Python OCR libraries or Python-based text extraction pipelines
  • 限制说明： 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.

评审后的下一步

先决定动作，再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”，而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核，以及是否已经进入工作流落地阶段。

安装路径

先进入安装与验证

如果这项技能值得继续，下一步应该先确认安装命令、CLI 写入路径与环境验证。
可信复核

回到可信合集再做一次复核

如果你还在比较不同技能或供应方，先回到可信合集做二次判断，而不是继续放大仓库噪音。
工作流

如果要进团队流转，转到工作流集合

当你的目标已经不是单个技能，而是团队协作、审批和可重复执行，就应该转到工作流合集继续比较。
实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致，便于搜索引擎理解页面内容。

? FAQ

python-ocr-expertise 是什么？

适用场景： Ideal for AI agents that need python ocr expertise. 本地化技能摘要： 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 Windsurf workflows.

如何安装 python-ocr-expertise？

运行命令：npx killer-skills add krzemienski/awesome-list-site/python-ocr-expertise。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

python-ocr-expertise 适用于哪些场景？

典型场景包括：适用任务： Applying Python OCR Expertise、适用任务： Applying APPLICABILITY GUARD、适用任务： Applying Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines。

python-ocr-expertise 支持哪些 IDE 或 Agent？

该技能兼容 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。可使用 Killer-Skills CLI 一条命令通用安装。

python-ocr-expertise 有哪些限制？

限制说明： Python-specific. Only activate for Python OCR libraries or Python-based text extraction pipelines.；限制说明： Only activate for Python OCR libraries or Python-based text extraction pipelines；限制说明： Requires repository-specific context from the skill documentation。

↓ 安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行：npx killer-skills add krzemienski/awesome-list-site/python-ocr-expertise。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    python-ocr-expertise 已启用，可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考，但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论，再决定是否继续查看上游仓库说明。

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

安装 python-ocr-expertise，这是一款面向AI agent workflows and automation的 AI Agent Skill。查看评审结论、使用场景与安装路径。

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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