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Atlas de IA

Análise de documentos e layout

Converter PDFs e digitalizações em dados estruturados

Dados e documentosIntermediário #38
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O texto completo é apresentado em inglês; o título e o resumo estão traduzidos.

O QUE ESTA CAPACIDADE SIGNIFICA

Takes a document — a PDF page image or a scan — and outputs structured content: paragraphs, heading levels, tables, figure captions, and the correct reading order. Unlike OCR it does more than transcribe: it decides what each block is and which comes first. Unlike information extraction it first rebuilds the layout skeleton rather than targeting specific fields.

Como é feita tecnicamente

A typical pipeline segments the page into regions — text blocks, headings, tables, figures — with detection or segmentation, then handles each: text blocks go through recognition, tables recover row-column and merged-cell structure, and formulas and charts are modelled separately. A layout model predicts block order, and multi-column or cross-column content needs dedicated sorting. Another route hands the whole page to a multimodal model that emits structured markup directly.

Produtos representativos

7

Organizações relacionadas

Usos típicos

  • Structuring invoices, contracts and forms
  • Table extraction from filings and research reports
  • Digitising archives and case files
  • Paper ingestion and knowledge-base building

Como avaliar se funciona bem

Layout element F1
Correctness of region classification such as heading, table and body
Table TEDS
Similarity of table structure trees, measuring row-column recovery
Reading-order accuracy
Agreement of the block sequence with the document logic

Limites e dificuldades

  • Cross-page tables and merged cells are hard to recover, misaligning rows or losing hierarchy
  • Handwritten notes, stamps and stickers disrupt segmentation and cause missed content
  • Skew, perspective and binding shadows in scans make column and table boundaries misjudged

Conceitos por trás