# Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework

arXiv: https://arxiv.org/abs/2006.00684 · HTML: https://mlsanity.com/paper/2006.00684/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting](https://mlsanity.com/paper/2105.07147/) (2021)
- [GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2201.00625/) (2022)
- [Recognizing Vector Graphics without Rasterization](https://mlsanity.com/paper/2111.03281/) (2021)
- [Symbol as Points: Panoptic Symbol Spotting via Point-based Representation](https://mlsanity.com/paper/2401.10556/) (2024)
- [SymPoint Revolutionized: Boosting Panoptic Symbol Spotting with Layer Feature Enhancement](https://mlsanity.com/paper/2407.01928/) (2024)
- [ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting](https://mlsanity.com/paper/2503.22346/) (2025)
- [CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings](https://mlsanity.com/paper/2412.07377/) (2024)
- [Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2505.23395/) (2025)
- [Knowledge driven Description Synthesis for Floor Plan Interpretation](https://mlsanity.com/paper/2103.08298/) (2021)
- [MuraNet: Multi-task Floor Plan Recognition with Relation Attention](https://mlsanity.com/paper/2309.00348/) (2023)
- [Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images](https://mlsanity.com/paper/2404.10985/) (2024)
- [AHMsys: An Automated HVAC Modeling System for BIM Project](https://mlsanity.com/paper/2407.01987/) (2024)
- [A document is worth a structured record: Principled inductive bias design for document recognition](https://mlsanity.com/paper/2507.08458/) (2025)
- [SkeySpot: Automating Service Key Detection for Digital Electrical Layout Plans in the Construction Industry](https://mlsanity.com/paper/2508.10449/) (2025)
- [Text-Enhanced Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2510.11091/) (2025)
- [Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings](https://mlsanity.com/paper/2607.12678/) (2026)
- [Handoff-H1: An Orchestrated Vision-Agent System for Material Quantity Takeoff from Construction Blueprints](https://mlsanity.com/paper/2608.15032/) (2026)

## Abstract (arXiv)

This papers focuses on symbol spotting on real-world digital architectural floor plans with a deep learning (DL)-based framework. Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. In this paper, we address all of the above issues by leveraging recent advances in DL and adapting an object detection framework based on the You-Only-Look-Once (YOLO) architecture. We propose a training strategy based on tiles, avoiding many issues particular to DL-based object detection networks related to the relative small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experiments on real-world floor plans demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity, even in the presence of heavy occlusion and clutter. Additional experiments on the public SESYD dataset confirm that our proposed approach can deal with various degradation and noise levels and outperforms other symbol spotting methods.