使用OCR和大型语言模型从植物样本标签中自动提取信息
1Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China Kunming Institute of Botany, Chinese Academy of Sciences Kunming China.
Biodiversity data journal
|January 29, 2026
概括
这项研究引入了一种使用OCR和LLM的自动化管道来提取草本体样本数据,大大提高了生物多样性研究的数字化效率.
科学领域:
- 植物学 植物学
- 生物多样性信息学 生物多样性信息学
- 数字策划数字策划
背景情况:
- 数字化草本标本对于生物多样性研究和数据共享至关重要.
- 手动转录是低效的,面临着大体积,多样化布局和手写的挑战.
- 现有的方法难以应对样品标签的复杂性.
研究的目的:
- 开发一个自动化管道,从草药馆样本标签中提取结构化信息.
- 克服手动数据输入的瓶,提高数字化效率.
- 从打印和手写标签中提取16个关键元数据字段.
主要方法:
- 将PaddleOCR引擎与DeepSeek大型语言模型 (LLM) 的集成.
- 从样本标签中提取结构化信息的管道的开发.
- 实施基于信任的手写标签质量控制机制.
主要成果:
- 对于打印标签,实现了95.4%的现场准确度.
- 该LLM有效地纠正了上游的OCR错误,显示了OCR信心和最终准确性之间的弱相关性 (r = 0.32).
- 该系统保留了手写标签的功能,承认其局限性.
结论:
- 拟议的混合管道提供了一种强大,可扩展和实用的解决方案,以加速植物收藏数字化.
- 局部图像处理确保了数据安全,只有文本的LLM解析提高了成本效益.
- 这种方法显著提高了生物多样性数据创建和共享效率,用于现实世界的应用.
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