Ensemble automated approaches for producing high-quality herbarium digital records.

ABSTRACT:

Premise

One of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.

Methods

We first showcase the development of a rule-based approach and compare outcomes with a large language model-based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule-based appro

SUBMITTER: Guralnick RP 

PROVIDER: S-EPMC11788903 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature