Update README.md

README.md

@@ -24,14 +24,10 @@ license: apache-2.0
 
 ## Model description 
 
-DocExplainer is a **first-step approach** to Visual Document Question Answering with bounding box localization.  
+DocExplainer is a an approach to Visual Document Question Answering with bounding box localization.  
 Unlike standard VLMs that only provide text-based answers, DocExplainer adds **visual evidence through bounding boxes**, making model predictions more interpretable.  
 It is designed as a **plug-and-play module** to be combined with existing Vision-Language Models (VLMs), decoupling answer generation from spatial grounding.  
 
-⚠️ **Important Note**:  
-This model is **not intended as a final solution**, but rather as a **baseline framework and proof of concept**. Its purpose is to highlight the gap between textual accuracy and spatial grounding in current VLMs, and to serve as a foundation for future research on interpretable document understanding.
-
-
 - **Authors:** Alessio Chen, Simone Giovannini, Andrea Gemelli, Fabio Coppini, Simone Marinai  
 - **Affiliations:**  [Letxbe AI](https://letxbe.ai/), [University of Florence](https://www.unifi.it/it)
 - **License:** CC-BY-4.0  
@@ -165,6 +161,24 @@ Example Output:
 </table> 
 
 
+## Performance 
+Evaluated on [BoundingDocs v2.0](https://huggingface.co/datasets/letxbe/BoundingDocs) dataset: 
+### Full DocExplainer Pipeline 
+
+| VLM Model       | ANLS ↑| IoU ↑ |
+| --------------- | ----- | ----- | 
+| SmolVLM2-2.2b   | 0.572 | 0.175 |
+| qwen2.5-vl-7b   | 0.689 | 0.188 |
+
+     
+### VLM-only Baseline (for comparison)
+| VLM Model       | ANLS ↑| IoU ↑ |
+| --------------- | ----- | ----- | 
+| SmolVLM2-2.2b   | 0.561 | 0.011 |
+| qwen2.5-vl-7b   | 0.720 | 0.038 |
+| Claude Sonnet 4 | 0.737 | 0.031 |
+
+
 ## Limitations
 - **Prototype only**: Intended as a first approach, not a production-ready solution.
 - **Dataset constraints**: Current evaluation is limited to cases where an answer fits in a single bounding box. Answers requiring reasoning over multiple regions or not fully captured by OCR cannot be properly evaluated.