Drawing a geometry figure is more painful than it looks.
It is not just opening software and dragging a few points around. You also need the segment proportions to look right, labels to stay out of the way, auxiliary lines to feel natural, and exported images to have enough resolution. Ten problems can take an afternoon. A full question bank can take a team months.
Today, the Dino-GSP Open Platform officially provides HTTP API services. Describe a geometry figure in words, and the AI understands the mathematical meaning, draws the figure, and returns the format you need. The input can be one sentence or a photo of a hand-drawn sketch. The output can be an image, a vector file, or code you can paste into LaTeX.
Who is this useful for now?
Teams that produce math content
You may have a hidden bottleneck: text problems can be entered in bulk, but geometry figures still need someone to draw them.
Imagine this workflow instead. When a new problem is added to your system, you send the problem description to the API. A few dozen seconds later, the geometry figure comes back and is stored with the problem automatically. The process is the same for one problem or ten thousand.
For projects that already have geometry figures, such as scanned past exam papers, you can upload the image as well. The AI recognizes the geometric structure and regenerates a precise editable version.
Product teams building math teaching tools
Almost every math teaching product eventually runs into the same requirement: geometry figures.
Building it yourself means training a specialized model and maintaining the data, algorithm, and engineering pipeline. That usually starts in months, and the result may still fall short of a specialized vertical service.
With Dino-GSP, your product sends the description and receives the figure and image. Integration is measured in days, not months. Your team can focus on the user experience instead of getting blocked by geometry figure production.
Researchers writing math papers or textbooks
Geometry figures in LaTeX are a very specific pain point.
TikZ is powerful, but it has a high learning curve and can be tedious to write. Drawing manually in GeoGebra and exporting works, but every change means going back through the drawing process.
Now there is another path: describe the geometry figure you want in natural language, including vertex positions, constraints, and labels. The system returns TikZ code that can be compiled directly. Paste it into the document and continue writing. A geometry figure should not slow down a paper or textbook.
Teachers and creators using AI for math
You may already have many hand-drawn geometry sketches: lesson notes, student work, and photos from reference books.
In the past, turning those sketches into polished figures meant redrawing them in software. Now you can upload the image directly. The AI recognizes the structure and outputs a precise geometry figure that can be adjusted further or exported immediately.
First capabilities now open
AI figure generation
Send a text description or a reference image, including a hand sketch, screenshot, or photo. The system understands the geometry structure and constraints, then generates a precise figure that remains interactive and editable.
The platform supports two model versions. One is optimized for bulk production of regular problems. The other is strengthened for complex synthesis problems and image recognition scenarios. You can switch between them by scenario and pay based on actual usage.
Geometry format export
For an existing geometry project, you can specify the output format and get the file you need:
- PNG image: for slides, exam papers, content platforms, and custom size or resolution needs
- SVG vector: for web embedding, responsive display, high-resolution print, and lossless scaling
- TikZ/LaTeX source code: ready to paste into a LaTeX document and compile for academic publishing
These are the first external API capabilities of the platform. We plan to keep expanding into more scenarios, including geometry understanding, geometry parsing, and multi-figure management. If you have a specific requirement or product scenario, tell us and we will prioritize it in the roadmap.
Why Dino-GSP can do this
Dino-GSP is not built on generic image generation. Its core is an AI model trained specifically for geometry figures. It understands mathematical semantics. When you say “draw a perpendicular from point C to AB,” it treats that as a geometric constraint instead of guessing from a visual prompt.
In internal evaluations covering about 2,000 standard geometry problems and 500 complex problems:
- Standard problem generation reached a 97% pass rate
- Complex synthesis problem generation reached an 89% pass rate
- Image recognition and reconstruction reached 93% accuracy
The output formats cover PNG images, SVG vectors, and TikZ/LaTeX source code, so the same capability can serve content platforms, product workflows, and academic publishing.
How to start
If you are an individual user or want to try the result first:
Log in to the Dino-GSP board and try AI figure generation directly in the editor.
If you are a product team or need batch production:
Go to the Open Platform, request access, read the integration docs, and evaluate how to bring geometry generation into your system. The two model tiers can be selected by scenario and charged by actual usage.
For questions, join the developer community or contact us directly.
The Dino-GSP Open Platform turns geometry drawing from manual work into a callable product capability.