Humans in the loop: Community science and machine learning synergies for overcoming herbarium digitization bottlenecks.

ABSTRACT:

Premise

Among the slowest steps in the digitization of natural history collections is converting imaged labels into digital text. We present here a working solution to overcome this long-recognized efficiency bottleneck that leverages synergies between community science efforts and machine learning approaches.

Methods

We present two new semi-automated services. The first detects and classifies typewritten, handwritten, or mixed labels from herbarium sheets. The second uses a workflow tuned for specimen labels to label text using optical character recognition (OCR). The label finder and classifier was built via humans-in-the-loop processes that utilize the community science Notes from Nature platform to develop training and validation data sets to feed into a machine learning

SUBMITTER: Guralnick R 

PROVIDER: S-EPMC10873811 | biostudies-literature | 2024 Jan-Feb

REPOSITORIES: biostudies-literature