FlowExtract: Procedural Knowledge Extraction from Maintenance Flowcharts

2026-04-08 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

FlowExtract is a novel pipeline designed to extract procedural knowledge from maintenance flowcharts, which are typically stored as static PDFs or scanned images in manufacturing facilities. These flowcharts, often compliant with ISO 5807 standards, contain critical information for asset lifecycle management but are inaccessible to modern operator support systems. The FlowExtract system addresses this by separating element detection from connectivity reconstruction. It utilizes YOLOv8 for node detection and EasyOCR for text extraction, complemented by a new edge detection method that analyzes arrowhead orientations and traces connecting lines to their source nodes. Evaluated on industrial troubleshooting guides, FlowExtract demonstrates high node detection accuracy and significantly surpasses vision-language model baselines in edge extraction, providing a practical solution for converting static procedural knowledge into queryable, graph-based representations.

Key takeaway

For Computer Vision Engineers or Research Scientists tasked with digitizing legacy documentation, FlowExtract offers a robust method to transform static maintenance flowcharts into queryable graph structures. This approach significantly improves upon general vision-language models for edge extraction, enabling the creation of structured, machine-readable procedural knowledge bases. You should consider integrating this pipeline to enhance operator support systems and streamline asset lifecycle management.

Key insights

FlowExtract converts static maintenance flowcharts into queryable directed graphs using specialized vision models.

Principles

• Separate element detection from connectivity.
• Analyze arrowheads for directed graph edges.

Method

FlowExtract employs YOLOv8 for node detection, EasyOCR for text extraction, and a novel edge detection method that traces lines backward from arrowheads to reconstruct connection topology in ISO 5807 flowcharts.

In practice

• Automate knowledge extraction from PDFs.
• Integrate procedural knowledge into support systems.

Topics

• FlowExtract
• Procedural Knowledge Extraction
• Maintenance Flowcharts
• Vision-Language Models
• YOLOv8

Code references

• guille-gil/FlowExtract

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Automation Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.