# LLM-based Control Code Generation using Image Recognition

arXiv: https://arxiv.org/abs/2311.10401 · HTML: https://mlsanity.com/paper/2311.10401/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2023.

## Built on by (most cited first, 10 of 10)

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- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [An Agentic Approach to Automatic Creation of P&ID Diagrams from Natural Language Descriptions](https://mlsanity.com/paper/2412.12898/) (2024)
- [Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation](https://mlsanity.com/paper/2404.14370/) (2024)
- [AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers](https://mlsanity.com/paper/2412.02410/) (2024)
- [From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper](https://mlsanity.com/paper/2503.07450/) (2025)
- [Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications](https://mlsanity.com/paper/2312.07069/) (2023)
- [Argus: A Multi-Agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationships](https://mlsanity.com/paper/2512.08326/) (2025)

## Abstract (arXiv)

LLM-based code generation could save significant manual efforts in industrial automation, where control engineers manually produce control logic for sophisticated production processes. Previous attempts in control logic code generation lacked methods to interpret schematic drawings from process engineers. Recent LLMs now combine image recognition, trained domain knowledge, and coding skills. We propose a novel LLM-based code generation method that generates IEC 61131-3 Structure Text control logic source code from Piping-and-Instrumentation Diagrams (P&IDs) using image recognition. We have evaluated the method in three case study with industrial P&IDs and provide first evidence on the feasibility of such a code generation besides experiences on image recognition glitches.