![[2602.17189] Texo: Formula Recognition within 20M Parameters](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17189] Texo: Formula Recognition within 20M Parameters
Summary
The paper presents Texo, a compact formula recognition model with 20 million parameters, achieving high performance comparable to larger models while enabling real-time inference on standard hardware.
Why It Matters
Texo's design allows for significant reductions in model size without sacrificing performance, making advanced formula recognition accessible on consumer-grade devices. This has implications for educational tools, research applications, and web-based implementations, democratizing access to AI capabilities.
Key Takeaways
- Texo achieves comparable performance to larger models like UniMERNet-T and PPFormulaNet-S.
- The model size is reduced by 80% and 65% respectively, enhancing efficiency.
- Real-time inference is possible on consumer-grade hardware, broadening accessibility.
- A web application has been developed to showcase Texo's capabilities.
- The approach emphasizes attentive design and effective vocabulary transfer.
Computer Science > Artificial Intelligence arXiv:2602.17189 (cs) [Submitted on 19 Feb 2026] Title:Texo: Formula Recognition within 20M Parameters Authors:Sicheng Mao View a PDF of the paper titled Texo: Formula Recognition within 20M Parameters, by Sicheng Mao View PDF HTML (experimental) Abstract:In this paper we present Texo, a minimalist yet highperformance formula recognition model that contains only 20 million parameters. By attentive design, distillation and transfer of the vocabulary and the tokenizer, Texo achieves comparable performance to state-of-the-art models such as UniMERNet-T and PPFormulaNet-S, while reducing the model size by 80% and 65%, respectively. This enables real-time inference on consumer-grade hardware and even in-browser deployment. We also developed a web application to demonstrate the model capabilities and facilitate its usage for end users. Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2602.17189 [cs.AI] (or arXiv:2602.17189v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.17189 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sicheng Mao [view email] [v1] Thu, 19 Feb 2026 09:14:32 UTC (327 KB) Full-text links: Access Paper: View a PDF of the paper titled Texo: Formula Recognition within 20M Parameters, by Sicheng MaoView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next >...
