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README.md

@@ -31,7 +31,7 @@ configs:
 # CHI-KNOW-PO — Line-Level HTR Ground-Truth for Chinese Historical Texts
 
 <p align="center">
-  <a href="https://doi.org/10.1007/978-3-031-70642-4_3"><img src="https://img.shields.io/badge/📄 Paper-ICDAR 2024-blue" alt="Paper"></a>
+  <a href="https://doi.org/10.1007/978-3-031-70642-4_3"><img src="https://img.shields.io/badge/Paper-ICDAR 2024-blue" alt="Paper"></a>
   <a href="https://github.com/calfa-co/chi-know-po"><img src="https://img.shields.io/badge/GitHub-PageXML dataset-green" alt="GitHub"></a>
   <a href="https://www.collexpersee.eu/projet/chi-know-po-corpus/"><img src="https://img.shields.io/badge/Project-CHI--KNOW--PO-orange" alt="Project"></a>
   <a href="https://calfa.fr"><img src="https://img.shields.io/badge/Platform-Calfa Vision-purple" alt="Calfa"></a>
@@ -47,11 +47,11 @@ This HuggingFace dataset provides **cropped line-level images** paired with thei
 
 ### Key Features
 
-- 🖼️ **13,634 cropped text-line images** from 325 annotated pages
-- ✍️ **104,769 transcribed characters** covering **5,589 unique sinograms**
-- 📚 **13 documents** spanning 9 genres: anthologies, encyclopedias, dictionaries, commentaries, collections, essays, and technical treatises
-- 🏷️ **Semantic metadata** per line: document ID, title (Chinese/English), type, author, edition, library, call number
-- 📐 **Stratified train/val/test splits** (80/10/10) by document
+- **13,634 cropped text-line images** from 325 annotated pages
+- **104,769 transcribed characters** covering **5,589 unique sinograms**
+- **13 documents** spanning 9 genres: anthologies, encyclopedias, dictionaries, commentaries, collections, essays, and technical treatises
+- **Semantic metadata** per line: document ID, title (Chinese/English), type, author, edition, library, call number
+- **Stratified train/val/test splits** (80/10/10) by document
 
 ## Dataset Composition
 
@@ -179,11 +179,11 @@ Working with this corpus presents several specific challenges:
 
 ## Related Resources
 
-- 📄 **Paper:** [Optimizing HTR and Reading Order Strategies for Chinese Imperial Editions with Few-Shot Learning](https://doi.org/10.1007/978-3-031-70642-4_3) (ICDAR 2024 Workshops)
-- 📦 **PageXML dataset (GitHub):** [calfa-co/chi-know-po](https://github.com/calfa-co/chi-know-po)
-- 🌐 **Project page:** [CHI-KNOW-PO Corpus (CollEx-Persée)](https://www.collexpersee.eu/projet/chi-know-po-corpus/)
-- 🔬 **GitLab (research):** [gitlab.huma-num.fr/chi-know-po](https://gitlab.huma-num.fr/chi-know-po)
-- 🖥️ **Annotation platform:** [Calfa Vision](https://vision.calfa.fr)
+- **Paper:** [Optimizing HTR and Reading Order Strategies for Chinese Imperial Editions with Few-Shot Learning](https://doi.org/10.1007/978-3-031-70642-4_3) (ICDAR 2024 Workshops)
+- **PageXML dataset (GitHub):** [calfa-co/chi-know-po](https://github.com/calfa-co/chi-know-po)
+- **Project page:** [CHI-KNOW-PO Corpus (CollEx-Persée)](https://www.collexpersee.eu/projet/chi-know-po-corpus/)
+- **GitLab (research):** [gitlab.huma-num.fr/chi-know-po](https://gitlab.huma-num.fr/chi-know-po)
+- **Annotation platform:** [Calfa Vision](https://vision.calfa.fr)
 
 ## Citation