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Atlas de IA

Análisis de documentos y maquetación

Convertir PDF y escaneos en datos estructurados

Datos y documentosIntermedio #38
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El texto completo se presenta en inglés; el título y el resumen están traducidos.

QUÉ SIGNIFICA ESTA CAPACIDAD

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.

Cómo se consigue técnicamente

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.

Productos representativos

7

Organizaciones 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

Cómo se evalúa

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

Límites y dificultades

  • 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

Conceptos detrás