Automated information extraction from plant specimen labels using OCR and large language models.

ABSTRACT: The digitization of herbarium specimens is crucial for advancing biodiversity research and data sharing. However, this process is often hindered by the inefficiency of manual transcription and the technical challenges posed by the massive volume of specimens, heterogeneous label layouts, and the prevalence of handwritten texts. To overcome these bottlenecks, this study proposed an automated pipeline that integrates the PadddleOCR engine with the DeepSeek large language model (LLM) for structured information extraction from specimen labels. The pipeline is designed to extract 16 key metadata fields from both printed and handwritten labels. Evaluated on a benchmark dataset, it achieved a high field-level accuracy of 95.4% for printed labels, demonstrating strong reliability. For handwritten

SUBMITTER: Wen J 

PROVIDER: S-EPMC12835874 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature