# Hespi: A pipeline for automatically detecting information from hebarium specimen sheets

arXiv: https://arxiv.org/abs/2410.08740 · HTML: https://mlsanity.com/paper/2410.08740/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 72 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Notes on Applicability of GPT-4 to Document Understanding](https://mlsanity.com/paper/2405.18433/) (2024)

## Built on by (most cited first, 1 of 1)

- [Automated pipeline for herbarium label digitization](https://mlsanity.com/paper/2608.28676/) (2026)

## Abstract (arXiv)

Specimen-associated biodiversity data are crucial for biological, environmental, and conservation sciences. A rate shift is needed to extract data from specimen images efficiently, moving beyond human-mediated transcription. We developed `Hespi' (HErbarium Specimen sheet PIpeline) using advanced computer vision techniques to extract pre-catalogue data from primary specimen labels on herbarium specimens. Hespi integrates two object detection models: one for detecting the components of the sheet and another for fields on the primary primary specimen label. It classifies labels as printed, typed, handwritten, or mixed and uses Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) for extraction. The text is then corrected against authoritative taxon databases and refined using a multimodal Large Language Model (LLM). Hespi accurately detects and extracts text from specimen sheets across international herbaria, and its modular design allows users to train and integrate custom models.