|
|
--- |
|
|
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** |
