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Synthetic STEM Training Data Ideas

This document outlines brainstormed ideas for using the latex2img-cli engine and related technologies to generate grounded synthetic training data for STEM AI models.

1. Symbol-Level Grounding (Grounded Math OCR)

The Concept: Generate high-resolution images of mathematical formulas where every individual symbol (operator, variable, digit) is mapped to a precise bounding box.

  • Implementation: Wrap each symbol in a unique identifier during the KaTeX rendering process. Use Puppeteer's getBoundingClientRect() to extract the [x, y, width, height] for every symbol.
  • Output: An image (.png) paired with a metadata file (.json) containing symbol locations and labels.
  • Utility: Training data for fine-grained math detection and layout analysis.

2. Grounded Circuits & Flowcharts (TikZ-to-Training)

The Concept: Programmatically generate scientific diagrams (circuits, logic gates, chemical structures, flowcharts) using the TikZ language.

  • Implementation: Write a generator that creates random but valid TikZ code. TikZ's internal coordinate system allows for direct extraction of "ground truth" labels (e.g., "This resistor is at pixel coordinates A, B").
  • Output: Clean SVG/PNG diagrams with a structured map of all components and their connections.
  • Utility: Teaching multimodal models to perform "Circuit Reasoning" or "Flowchart Tracing."

3. Synthetic Data Visualization (Chart-to-Data)

The Concept: Create a bridge between raw numerical data and visual charts to train models in "Visual Data Inverse-Extraction."

  • Implementation: Use D3.js or Chart.js inside the Puppeteer engine to render line graphs, histograms, and scatter plots using synthetic datasets.
  • Output: The rendered chart image + a JSON file mapping raw data points to their specific pixel locations on the rendered axes.
  • Utility: Training models to accurately convert images found in PDFs back into raw CSV/spreadsheet data.

4. Grounded Step-by-Step "Chain of Thought" Images

The Concept: Visualizing the logical flow of a derivation or proof.

  • Implementation: Generate a sequence of images representing steps in a math problem (e.g., $2x + 5 = 15 \Rightarrow 2x = 10 \Rightarrow x = 5$).
  • Grounding: In each frame, use a "highlight" or "glow" effect on the terms that are currently being operated on.
  • Utility: Training models to understand the procedural changes in an equation over time rather than just static snapshots.

5. Multi-Style Handwriting & Artifact Synthesis

The Concept: Generating "messy" but grounded data to bridge the reality gap between digital typeset and physical handwriting.

  • Implementation: Apply chaotic SVG filters (blur, ink bleed, variable pressure) and randomized affine transformations (skew, tilt, jitter) to the text elements.
  • Grounding: The model is trained on the "messy" image but evaluated against the perfectly clean LaTeX "gold standard."
  • Utility: Improving OCR robustness for scanned student papers, homework, and historical scientific manuscripts.

Brainstormed: 2026-03-17