Towards Automated P&ID Digitization: Graph-Based OCR Consolidation and Global Symbol-Tag Association
TL;DR
This paper presents an end-to-end framework for automatic instrument-tag extraction and association from scanned P&IDs, and demonstrates accurate tag reconstruction, highly reliable symbol-tag associations, and a substantial reduction in manual annotation effort, providing an effective foundation for large-scale P&ID digitization.
Abstract
Piping and Instrumentation Diagrams (P&IDs) are essential engineering documents, but many remain available only as scanned PDFs, limiting their integration into digital workflows. Automatic extraction of structured information from these drawings is challenging due to large document sizes, small text annotations, and ambiguous symbol-tag relationships. This paper presents an end-to-end framework for automatic instrument-tag extraction and association from scanned P&IDs. A tiled OCR strategy with graph-based text merging improves text completeness and reconstructs fragmented engineering tags. For symbol detection, an RF-DETR model fine-tuned on the Dataset-P&ID benchmark achieves 99.96% mAP@50 and 99.97% precision, while SAHI-based inference slicing improves performance on large drawings. To automate symbol-tag association, we formulate the problem as a minimum-cost bipartite matching task that combines geometric, semantic, and spatial cues and solves it globally using the Hungarian algorithm. Results on Dataset-P&ID demonstrate accurate tag reconstruction, highly reliable symbol-tag associations, and a substantial reduction in manual annotation effort, providing an effective foundation for large-scale P&ID digitization. The code is available at https://github.com/dimitri009/STA.