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---
datasets:
- prithivMLmods/Document-Type-Detection
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Document
- Classification
- finance
---
# **Document-Type-Detection**
> **Document-Type-Detection** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect and classify **types of documents** from scanned or photographed images. This model is helpful for **automated document sorting**, **OCR pipelines**, and **digital archiving systems**.
```py
Classification Report:
precision recall f1-score support
Advertisement-Doc 0.8940 0.8940 0.8940 2000
Hand-Written-Doc 0.9168 0.9310 0.9238 2000
Invoice-Doc 0.9026 0.8940 0.8983 2000
Letter-Doc 0.8380 0.8820 0.8594 2000
News-Article-Doc 0.9258 0.8800 0.9023 2000
Resume-Doc 0.9425 0.9340 0.9382 2000
accuracy 0.9025 12000
macro avg 0.9033 0.9025 0.9027 12000
weighted avg 0.9033 0.9025 0.9027 12000
```
---
## **Label Classes**
The model classifies images into the following document types:
```
0: Advertisement-Doc
1: Hand-Written-Doc
2: Invoice-Doc
3: Letter-Doc
4: News-Article-Doc
5: Resume-Doc
```
---
## **Installation**
```bash
pip install transformers torch pillow gradio
```
---
## **Example Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Document-Type-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# ID to label mapping
id2label = {
"0": "Advertisement-Doc",
"1": "Hand-Written-Doc",
"2": "Invoice-Doc",
"3": "Letter-Doc",
"4": "News-Article-Doc",
"5": "Resume-Doc"
}
def detect_doc_type(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=detect_doc_type,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=6, label="Document Type"),
title="Document-Type-Detection",
description="Upload a document image to classify it as one of: Advertisement, Hand-Written, Invoice, Letter, News Article, or Resume."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Applications**
* **Automated Document Sorting**
* **Digital Libraries and Archives**
* **OCR Preprocessing**
* **Enterprise Document Management** |