---
datasets:
- letxbe/BoundingDocs
language:
- en
pipeline_tag: visual-question-answering
tags:
- Visual-Question-Answering
- Question-Answering
- Document
license: apache-2.0
---


<div align="center">

<h1>DocExplainerV0: Visual Document QA with Bounding Box Localization</h1>

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![arXiv](https://img.shields.io/badge/arXiv-2501.03403-b31b1b.svg)]()
[![HuggingFace](https://img.shields.io/badge/🤗%20Hugging%20Face-Datasets-yellow)](https://huggingface.co/letxbe/DocExplainerV0)

</div>

## Model description 

DocExplainerV0 is a **first-step approach** to Visual Document Question Answering with bounding box localization.  
Unlike standard VLMs that only provide text-based answers, DocExplainerV0 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  
- **Paper:** ["Towards Reliable and Interpretable Document Question Answering via VLMs"](https://arxiv.org/abs/2501.03403) by Alessio Chen et al.  

<div align="center">
<img src="https://cdn.prod.website-files.com/655f447668b4ad1dd3d4b3d9/664cc272c3e176608bc14a4c_LOGO%20v0%20-%20LetXBebicolore.svg" alt="letxbe ai logo" width="200">
<img src="https://www.dinfo.unifi.it/upload/notizie/Logo_Dinfo_web%20(1).png" alt="Logo Unifi" width="200">
</div>
---

## Model Details

DocExplainerV0 is a fine-tuned SigLIP-based regressor that predicts bounding box coordinates for answer localization in document images. The system operates in a two-stage process:

1. **Question Answering**: Any VLM processes the document image and question to generate a textual answer.  
2. **Bounding Box Explanation**: DocExplainerV0 takes the image, question, and generated answer to predict the coordinates of the supporting evidence.  


## Model Architecture 
DocExplainerV0 builds on [SigLIP2](https://huggingface.co/google/siglip2-giant-opt-patch16-384) visual and text embeddings.

![https://i.postimg.cc/BQR21cxp/Blank-diagram-5.png](https://i.postimg.cc/BQR21cxp/Blank-diagram-5.png)

## Training Procedure
- Visual and textual embeddings from SigLiP2 are projected into a shared latent space, fused via fully connected layers.
- A regression head outputs normalized coordinates `[x1, y1, x2, y2]`.  
- **Backbone**: SigLiP2 (frozen).  
- **Loss Function**: Smooth L1 (Huber loss) applied to normalized coordinates in [0,1].  

#### Training Setup
- **Dataset**: [BoundingDocs v2.0](https://huggingface.co/datasets/letxbe/BoundingDocs)  
- **Epochs**: 20  
- **Optimizer**: AdamW (default settings)  
- **Hardware**: 1 × NVIDIA L40S-1-48G GPU  
- **Model Selection**: Best checkpoint chosen by highest mean IoU on the validation split.  



## Quick Start 

Here is a simple example of how to use `DocExplainer` to get an answer and its corresponding bounding box from a document image.

```python
from PIL import Image
import requests
from transformers import AutoModel

# Load example document image
url = "https://datasets-server.huggingface.co/cached-assets/letxbe/BoundingDocs/--/47db6d2b6af0aadfd082591a8445d0f47c3b8d61/--/default/test/7/doc_images/image-1d100e9.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
question = "What is the invoice number?"
answer = "3Y8M2d-846" # generate it with any VLM

explainer = AutoModel.from_pretrained("letxbe/DocExplainerv0", trust_remote_code=True)
bbox = explainer.predict(image, answer)
print(f"Bounding box: {bbox}")  # [x1, y1, x2, y2]
```


<table> 
  <tr> 
    <td width="50%" valign="top">
Example Output:

**Question**: What is the invoice number? <br>
**Answer**: 3Y8M2d-846<br><br>
**Predicted BBox**: [0.6353235244750977, 0.03685223311185837, 0.8617828488349915, 0.058749228715896606] <br>
    </td> 
    <td width="50%" valign="top">
      Visualized Answer Location:
    <img src="https://i.postimg.cc/0NmBM0b1/invoice-explained.png" alt="Invoice with predicted bounding box" width="100%"> 
    </td> 
  </tr> 
</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.

## Citation
If you use this model in your research, please cite:
```
bibtex@misc{docexplainer2025,
  title={Towards Reliable and Interpretable Document Question Answering via VLMs},
  author={[Your Name]},
  year={2025},
  url={}
}
```