Update README.md

README.md

@@ -1,11 +1,15 @@
 ---
 pipeline_tag: image-to-text
 tags:
-- 'arxiv:2504.03349'
-- 'github_repo:https://github.com/FactoDeepLearning/META-DAN'
+  - 'arxiv:2504.03349'
 ---
+
+# Meta-DAN
+
 **<span style="color: #A6351B;">Warning: this project is under a research/academic usage only license, preventing any usage for commercial purposes without permission.</span>**
 
+
+
 This repository contains official trained model weights from the paper:  
 Denis Coquenet, "Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition", Pattern Recognition, 2026.
 
@@ -15,4 +19,20 @@ The source code is available [here](https://github.com/FactoDeepLearning/META-DA
 Weights are given for the following models: DAN (TPAMI 2023), Faster DAN (ICDAR 2023), MT-DAN (PR 2026), W-DAN (PR 2026) and Meta-DAN (PR 2026).  
 Pre-trained weights at line level are also provided. 
 
-Each models is either trained on a single dataset (BRESSAY, CASIA2, Eparchos, Esaposalles, IAM, MAURDOR, READ2016, RIMES, ScribbleLens) or on a set of latin languages (IAM+BRESSAY+READ2016+Espossales+ScribbleLens).
+Each models is either trained on a single dataset (BRESSAY, CASIA2, Eparchos, Esaposalles, IAM, MAURDOR, READ2016, RIMES, ScribbleLens) or on a set of latin languages (IAM+BRESSAY+READ2016+Espossales+ScribbleLens).
+
+## Citation
+
+**BibTeX**
+
+```
+@article{Coquenet2026,
+    author={ Denis Coquenet },
+    journal={Pattern Recognition},
+    title={Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition},
+    year={2026},
+    pages={113373},
+    volume={117},
+    doi={10.1016/j.patcog.2026.113373},
+} 
+```