2000万参数模型实现高精度公式识别，轻量部署更便捷
Texo: Formula Recognition within 20M Parameters
- 通过词表与分词器的精心迁移设计，实现小模型高精度
- 性能媲美主流模型，体积缩小65%以上，支持实时推理
- 适合需要轻量级公式识别的应用场景，如网页端工具
本文提出Texo，一个仅含2000万参数的轻量级高精度公式识别模型。通过精心设计的词表与分词器迁移、知识蒸馏与迁移学习，Texo在性能上达到UniMERNet-T和PPFormulaNet-S相当水平，模型规模分别减少80%和65%。该模型可在消费级硬件上实现实时推理，甚至支持浏览器内部署。我们还开发了演示网页应用，便于终端用户使用。
原文摘要 · Abstract (English)
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.
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