PP-FormulaNet高效精准识别公式，适配高精度与高速场景。
PP-FormulaNet: Bridging Accuracy and Efficiency in Advanced Formula Recognition
- 设计双模型：大版追求精度，小版优化速度，兼顾不同需求。
- 大模型准确率比UniMERNet高6%，小模型速度超16倍于基准。
- 配套数据挖掘系统，支持大规模高质量公式训练数据生成。
公式识别是文档智能中的关键任务，旨在将文档图像中的数学表达式转换为计算机可处理的结构化符号格式，常用格式为LaTeX。本文提出PP-FormulaNet，一种在准确率和效率上均达到顶尖水平的公式识别模型。针对不同应用需求，我们开发了两个专用模型：适用于高精度场景的PP-FormulaNet-L，以及面向高效率环境的PP-FormulaNet-S。大量实验表明，PP-FormulaNet-L的准确率显著优于主流模型UniMERNet，提升达6%；而PP-FormulaNet-S的运行速度超过16倍于基准。这些改进使模型可无缝集成至包含复杂公式的各类文档处理系统中。此外，我们还构建了一套公式挖掘系统，能够提取海量高质量公式数据，进一步增强模型的鲁棒性与适用性。代码与模型已公开于PaddleOCR（https://github.com/PaddlePaddle/PaddleOCR）和PaddleX（https://github.com/PaddlePaddle/PaddleX）。
原文摘要 · Abstract (English)
Formula recognition is an important task in document intelligence. It involves converting mathematical expressions from document images into structured symbolic formats that computers can easily work with. LaTeX is the most common format used for this purpose. In this work, we present PP-FormulaNet, a state-of-the-art formula recognition model that excels in both accuracy and efficiency. To meet the diverse needs of applications, we have developed two specialized models: PP-FormulaNet-L, tailored for high-accuracy scenarios, and PP-FormulaNet-S, optimized for high-efficiency contexts. Our extensive evaluations reveal that PP-FormulaNet-L attains accuracy levels that surpass those of prominent models such as UniMERNet by a significant 6%. Conversely, PP-FormulaNet-S operates at speeds that are over 16 times faster. These advancements facilitate seamless integration of PP-FormulaNet into a broad spectrum of document processing environments that involve intricate mathematical formulas. Furthermore, we introduce a Formula Mining System, which is capable of extracting a vast amount of high-quality formula data. This system further enhances the robustness and applicability of our formula recognition model. Code and models are publicly available at PaddleOCR(https://github.com/PaddlePaddle/PaddleOCR) and PaddleX(https://github.com/PaddlePaddle/PaddleX).
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