The Image Extractor

We are building a tool that automatically processes images from books. Our system will:

• Segment images from scanned pages
• Generate captions using AI
• Enhance image understanding with deep learning

Using advanced deep learning techniques, we aim to make historical and printed book images more accessible and usable.

This project will help researchers, libraries, and digital archives extract and analyze visual content effortlessly.

First Module: Image Feature Extractor

This project extracts image features using a Vision Transformer (ViT) model from Timm and provides multiple ways to process images:

• ✅ Extract features from a single image
• ✅ Find similar images in a dataset
• ✅ Generate tags for images

📌 Requirements

Before running the project, make sure you have installed the dependencies:

pip install torch torchvision timm pillow fiftyone requests

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🛠️ How to Use

1️⃣ Extract Features from a Single Image

Run the following command to extract features from an image:

python main.py --extract path/to/image.jpg

• Extracts features using the Vision Transformer (ViT) model.
• Saves the extracted features for further analysis.

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2️⃣ Find Similar Images in a Dataset

To find similar images within a dataset, run:

python main.py --find path/to/image.jpg path/to/dataset

• Computes image embeddings.
• Finds similar images based on feature similarity.

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3️⃣ Generate Tags for an Image

To generate tags based on an image:

python main.py --tag path/to/image.jpg

• Uses deep learning to assign relevant tags.

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🔧 Changing the Model

By default, the script uses vit_base_patch16_224. To use another model, specify it like this:

python main.py --extract path/to/image.jpg --model resnet50

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📌 Notes

• The extracted features can be saved to a file or used for further analysis.
• If the dataset or image path does not exist, the script will display an error.
• No FastAPI is needed; all operations run as simple Python scripts.

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