Summary of Hespi: a Pipeline For Automatically Detecting Information From Hebarium Specimen Sheets, by Robert Turnbull and Emily Fitzgerald and Karen Thompson and Joanne L. Birch
Hespi: A pipeline for automatically detecting information from hebarium specimen sheets
by Robert Turnbull, Emily Fitzgerald, Karen Thompson, Joanne L. Birch
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Hespi pipeline is a machine learning solution that automates the extraction of data from digital images of herbarium specimens, reducing the reliance on human-mediated transcription. The pipeline integrates two object detection models to detect text-based labels and data fields, followed by Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) for data extraction. A multimodal Large Language Model (LLM) is used for correcting recognized text against authoritative databases of taxon names. This approach shows promising results in detecting and extracting text from specimen sheet images from international herbaria, with potential applications in biological, environmental, climate, and conservation sciences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a special computer program called Hespi that can help scientists by automatically reading data from pictures of old plant specimens. These plants are important for understanding the natural world and how it’s changing over time. The program uses a combination of algorithms to find words on the pictures, correct any mistakes, and provide accurate information about what species of plants were studied. This will make it easier for scientists to study and understand the data they need. |
Keywords
» Artificial intelligence » Large language model » Machine learning » Object detection
