Update README.md

README.md

@@ -3,4 +3,21 @@ license: gpl-3.0
 base_model:
 - naver-clova-ix/donut-base
 pipeline_tag: visual-document-retrieval
----
+---
+
+# HeR-T: Herbarium specimen label Recognition Transformer  
+
+## 📃 Paper
+Application of computer vision to the automated extraction of metadata from natural history specimen labels: A case study on herbarium specimens (Under Review)
+
+## 💁 Authors
+Zacchigna, Jacopo; Liu, Weiwei; Pellegrino, Felice Andrea; Peron, Adriano; Roma-Marzio, Francesco; Peruzzi, Lorenzo; Martellos, Stefano
+
+## 🚀 Overview  
+HeR-T (Herbarium specimen label Recognition Transformer) is a fine-tuned vision-language model designed for automated metadata extraction of history specimen labels, especially herbarium specimen labels. It leverages Donut-base and has been fine-tuned with 55,089 herbarium specimen images from the Herbarium of the University of Pisa (international acronym PI). 
+
+## 🔥 Features  
+- **Fine-tuned on** specimen images from the Herbarium of the University of Pisa for automated metadata extraction of history specimen labels
+- **Supports** image inputs with labels containing printed, handwritten, or mixed-format texts  
+- **Evaluation**: Tree Edit Distance (TED) accuracy score with the formula max(0, 1−TED(pr, gt)/TED(φ, gt)), where gt, pr, and φ stand for ground truth, prediction, and empty trees respectively 
+- **Pre-trained weights** are loaded from Donut-base (naver-clova-ix/donut-base)