SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches

Fuente: arXiv
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Hauptverfasser: Tan, Cheng, Chen, Qi, Wei, Jingxuan, Wu, Gaowei, Gao, Zhangyang, Li, Siyuan, Yu, Bihui, Guo, Ruifeng, Li, Stan Z.
Format: Preprint
Veröffentlicht: 2025
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author Tan, Cheng
Chen, Qi
Wei, Jingxuan
Wu, Gaowei
Gao, Zhangyang
Li, Siyuan
Yu, Bihui
Guo, Ruifeng
Li, Stan Z.
author_facet Tan, Cheng
Chen, Qi
Wei, Jingxuan
Wu, Gaowei
Gao, Zhangyang
Li, Siyuan
Yu, Bihui
Guo, Ruifeng
Li, Stan Z.
contents Hand-drawn sketches are a natural and efficient medium for capturing and conveying ideas. Despite significant advancements in controllable natural image generation, translating freehand sketches into structured, machine-readable diagrams remains a labor-intensive and predominantly manual task. The primary challenge stems from the inherent ambiguity of sketches, which lack the structural constraints and semantic precision required for automated diagram generation. To address this challenge, we introduce SketchAgent, a multi-agent system designed to automate the transformation of hand-drawn sketches into structured diagrams. SketchAgent integrates sketch recognition, symbolic reasoning, and iterative validation to produce semantically coherent and structurally accurate diagrams, significantly reducing the need for manual effort. To evaluate the effectiveness of our approach, we propose the Sketch2Diagram Benchmark, a comprehensive dataset and evaluation framework encompassing eight diverse diagram categories, such as flowcharts, directed graphs, and model architectures. The dataset comprises over 6,000 high-quality examples with token-level annotations, standardized preprocessing, and rigorous quality control. By streamlining the diagram generation process, SketchAgent holds great promise for applications in design, education, and engineering, while offering a significant step toward bridging the gap between intuitive sketching and machine-readable diagram generation. The benchmark is released at https://huggingface.co/datasets/DiagramAgent/Sketch2Diagram-Benchmark.
format Preprint
id arxiv_https___arxiv_org_abs_2508_01237
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches
Tan, Cheng
Chen, Qi
Wei, Jingxuan
Wu, Gaowei
Gao, Zhangyang
Li, Siyuan
Yu, Bihui
Guo, Ruifeng
Li, Stan Z.
Artificial Intelligence
Hand-drawn sketches are a natural and efficient medium for capturing and conveying ideas. Despite significant advancements in controllable natural image generation, translating freehand sketches into structured, machine-readable diagrams remains a labor-intensive and predominantly manual task. The primary challenge stems from the inherent ambiguity of sketches, which lack the structural constraints and semantic precision required for automated diagram generation. To address this challenge, we introduce SketchAgent, a multi-agent system designed to automate the transformation of hand-drawn sketches into structured diagrams. SketchAgent integrates sketch recognition, symbolic reasoning, and iterative validation to produce semantically coherent and structurally accurate diagrams, significantly reducing the need for manual effort. To evaluate the effectiveness of our approach, we propose the Sketch2Diagram Benchmark, a comprehensive dataset and evaluation framework encompassing eight diverse diagram categories, such as flowcharts, directed graphs, and model architectures. The dataset comprises over 6,000 high-quality examples with token-level annotations, standardized preprocessing, and rigorous quality control. By streamlining the diagram generation process, SketchAgent holds great promise for applications in design, education, and engineering, while offering a significant step toward bridging the gap between intuitive sketching and machine-readable diagram generation. The benchmark is released at https://huggingface.co/datasets/DiagramAgent/Sketch2Diagram-Benchmark.
title SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches
topic Artificial Intelligence
url https://arxiv.org/abs/2508.01237