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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

S

Sicheng Mao

T´el´ecom Paris, Institut Polytechnique de Paris

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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."

AI OCR SolutionScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

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

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2026Cheng Cui, Ting Sun et al.
[2]
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[3]
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[4]
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2025Hongen Liu, Cheng Cui et al.
[5]
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2024Linke Ouyang, Yuan Qu et al.
[6]
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[7]
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[10]
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[11]
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[12]
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[13]
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[14]
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[15]
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[16]
DETRs Beat YOLOs on Real-time Object Detection
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[17]
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition
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[18]
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2021Igor Samenko, Alexey Tikhonov et al.
[19]
Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning
2021Xiaohang Bian, Bo Qin et al.
[20]
OCR-Free Document Understanding Transformer
2021Geewook Kim, Teakgyu Hong et al.

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