👁 Doc.png

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.

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

👁 download (2).png

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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

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Installation

pip install transformers torch pillow gradio

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Example Inference Code

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()

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Applications

• Automated Document Sorting
• Digital Libraries and Archives
• OCR Preprocessing
• Enterprise Document Management

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 8 items • Updated 1 day ago • 3