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Construa pipelines de pré-processamento OCR e análise de layout…

Use o deepdoctection quando um fluxo de trabalho de agente precisar de um pipeline Python local para análise de layout de documentos, OCR, reconhecimento de tabelas e extração em nível de página antes da ingestão por LLM.

O que é Construa pipelines de pré-processamento OCR e análise de layout…?

Este skill capacita agentes de IA a construir pipelines Python locais para pré-processamento de documentos usando a biblioteca deepdoctection. Ele é especialmente útil quando um agente precisa extrair texto, detectar estruturas de layout (como títulos, parágrafos, tabelas) e realizar reconhecimento óptico de caracteres (OCR) em PDFs e imagens de documentos escaneados, antes de enviar o conteúdo para um modelo de linguagem de grande porte (LLM). O skill oferece uma instalação simplificada com dependências como `timm`, `transformers`, `python-doctr` e o próprio `deepdoctection`. Ele suporta plataformas como Claude Code, Cursor ou Codex, e funciona baseando-se no analisador embutido da biblioteca, que decompõe documentos em estruturas de layout para extração eficiente. Os requisitos incluem Python >= 3.10 e um ambiente com as dependências instaladas. A documentação completa está disponível em [deepdoctection.readthedocs.io](https://deepdoctection.readthedocs.io/).

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/build-ocr-and-layout-analysis-preprocessing-pipelines-with-deepdoctection
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Construa pipelines de pré-processamento OCR e análise de layout com deepdoctection

Use o deepdoctection quando um fluxo de trabalho de agente precisar de um pipeline Python local para análise de layout de documentos, OCR, reconhecimento de tabelas e extração em nível de página antes da ingestão por LLM.

Pré-requisitos

Ambiente Python; pacote deepdoctection; dependências selecionadas de OCR/layout/modelo; PDFs locais ou imagens de documentos escaneados

Instalação

Use o caminho de instalação upstream ou configuração que corresponda ao seu ambiente:

  • uv pip install timm # necessário para a configuração padrão
  • uv pip install transformers
  • uv pip install python-doctr
  • uv pip install deepdoctection

Requisitos e ressalvas do upstream:

  • deepdoctection é uma biblioteca Python que orquestra a análise de layout de documentos PDF e escaneados, OCR e extração de dados de documentos.
  • python
  • Python >= 3.10

Notas de uso básico ou primeiros passos:

Documentação

Fonte

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add executable smoke tests for shell scripts and CLIs before ref

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Add graph-backed memory and context retrieval to agent workflows

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Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

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Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

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Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

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