Formula-Text-Detection

Formula-Text-Detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for binary image classification. It is built using the SiglipForImageClassification architecture to distinguish between mathematical formulas and natural text in document or image regions.

Note: This model works best with plain text or formulas using the same font style

Classification Report:
              precision    recall  f1-score   support

     formula     0.9983    1.0000    0.9991      6375
        text     1.0000    0.9980    0.9990      5457

    accuracy                         0.9991     11832
   macro avg     0.9991    0.9990    0.9991     11832
weighted avg     0.9991    0.9991    0.9991     11832

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SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

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Label Space: 2 Classes

The model classifies each input image into one of the following categories:

Class 0: "formula"
Class 1: "text"

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Install Dependencies

pip install -q transformers torch pillow gradio

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Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Formula-Text-Detection"  # Replace with your model path if different
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
    "0": "formula",
    "1": "text"
}

def classify_formula_or_text(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=classify_formula_or_text,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Formula or Text"),
    title="Formula-Text-Detection",
    description="Upload an image region to classify whether it contains a mathematical formula or natural text."
)

if __name__ == "__main__":
    iface.launch()

Demo Inference

Text

Formula

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Intended Use

Formula-Text-Detection can be used in:

• OCR Preprocessing – Improve document OCR accuracy by separating formulas from text.
• Scientific Document Analysis – Automatically detect mathematical content.
• Educational Platforms – Classify and annotate scanned materials.
• Layout Understanding – Help AI systems interpret mixed-content documents.