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Founder's Pitch
"Texo is a lightweight formula recognition model that runs efficiently on consumer-grade hardware and is ready for real-time in-browser deployment."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
Formula recognition is crucial for converting complex mathematical expressions into a digital format that can be used in note-taking, academic writing, and especially in the preprocessing stages of training large language models.
Product Angle
The core of Texo, with its small size and ability to run in-browser, can be turned into a plugin or extension for document processors or educational software to automate and enhance mathematical content creation.
Disruption
By offering a lightweight and fast alternative, Texo could replace larger, more complex formula recognition tools, especially in devices with limited computational capabilities.
Product Opportunity
With increasing use of digital documents in academia and research, there's a significant market opportunity in the education and research sector for tools that simplify the handling of mathematical expressions. Educational technology companies or research software providers could integrate Texo to enhance their offerings.
Use Case Idea
An API for seamless integration of formula recognition into document editing software, allowing instant conversion of written equations into LaTeX or MathML.
Science
Texo reduces the parameter size of formula recognition models by leveraging vocabulary distillation and transfer. It employs a CNN-based encoder and a lightweight Transformer-based decoder to efficiently recognize mathematical expressions while maintaining accuracy.
Method & Eval
Texo was evaluated against existing state-of-the-art models using the CDM score on the UniMER dataset, demonstrating comparable performance with only 20M parameters and achieving faster inference speeds.
Caveats
Texo, being minimalist, may struggle with exceptionally complex or novel mathematical expressions not covered by its training data. Its accuracy depends on the quality of the distillation and transfer process.
Author Intelligence
Sicheng Mao
T´el´ecom Paris, Institut Polytechnique de Paris
sicheng.mao@telecom-paris.fr