Principle:Unstructured IO Unstructured Strategy Selection
| Knowledge Sources | |
|---|---|
| Domains | Document_Processing, Preprocessing |
| Last Updated | 2026-02-12 00:00 GMT |
Overview
A decision mechanism that selects the optimal document parsing strategy based on accuracy requirements, performance constraints, and available system resources.
Description
Strategy selection determines how a document is analyzed during partitioning. Different strategies trade off between speed, accuracy, and resource requirements. A fast text-extraction strategy processes documents quickly but misses layout information, while a high-resolution strategy uses computer vision models for accurate layout detection but requires GPU resources and more processing time.
This principle addresses the fundamental tension in document processing between extraction quality and computational cost. By exposing strategy as a configurable parameter, the system allows users to tune the pipeline for their specific requirements.
Usage
Use this principle when designing document processing pipelines where you need to control the trade-off between extraction quality and processing speed. The strategy choice should be driven by the document characteristics (scanned vs. digital, text-heavy vs. layout-heavy) and the downstream task requirements (full-text search vs. layout-preserving extraction).
Theoretical Basis
Document processing strategies map to fundamentally different extraction approaches:
Text extraction (fast): Directly extracts embedded text from digital documents using format-specific parsers (pdfminer for PDF, python-docx for DOCX). This is the fastest approach but cannot handle scanned documents or preserve spatial layout.
Layout detection (hi_res): Uses computer vision models (YOLOX, Detectron2) to detect document regions (titles, paragraphs, tables, figures) from page images. This preserves spatial relationships and handles scanned documents but requires ML inference.
OCR extraction (ocr_only): Applies Optical Character Recognition (Tesseract) to convert page images to text. Designed for scanned documents where embedded text is unavailable.
Automatic selection (auto): Examines each page to determine whether it contains extractable text. Pages with embedded text use the fast strategy; pages without use hi_res or OCR.
Pseudo-code logic:
# Abstract strategy selection
if strategy == "auto":
for page in document:
if page.has_extractable_text():
use_fast_extraction(page)
else:
use_hires_extraction(page)
elif strategy == "fast":
use_fast_extraction(document)
elif strategy == "hi_res":
use_hires_extraction(document)
elif strategy == "ocr_only":
use_ocr_extraction(document)