pdf segmentation

Introduction

The PDF Segmentation model is designed to extract paragraphs from PDF documents. It utilizes PDF features to determine text and paragraph structure, providing details such as page number, position, size, and the text content itself. A more advanced version of this service is available under the PDF Document Layout Analysis on Hugging Face.

Architecture

The architecture leverages PDF-specific features to analyze and segment the document into distinct paragraphs. This involves recognizing structural elements such as page numbers and content positioning to accurately extract text blocks.

Training

Details on the training process are not specified; however, the model likely uses machine learning techniques to learn the layout and structure of PDF documents for effective segmentation.

Guide: Running Locally

1. Clone the Repository: Use Git to clone the PDF Segmentation repository from Hugging Face.
2. Install Dependencies: Ensure all necessary Python libraries and tools are installed. Typically, this involves setting up a Python environment and installing libraries like PyPDF2 or similar.
3. Run the Model: Execute the scripts provided in the repository to process PDF files and extract paragraphs.

For enhanced performance, consider using cloud GPUs from providers such as AWS, Google Cloud, or Azure to handle large datasets or complex documents.

License

The model is distributed under the OpenRAIL license, allowing open use with some conditions.