Real-Time Summarization of Text, Images, and

 Documents Using Advanced Multimodal AI

 Techniques


In the era of information overload, the ability to quickly and accurately summarize content from various sources—text, images, scanned files, and documents—is vital. This project presents a powerful Universal Content Summarization System that leverages Google Gemini 1.5 and Large Language Models (LLMs) to generate real-time, context-aware summaries across multiple content types.


Project Overview

The system integrates Natural Language Processing (NLP), Computer Vision, and Multimodal AI Techniques to provide dynamic summarization capabilities for:

  • Raw text content

  • Visual media (images)

  • Digital and scanned documents (PDFs, DOCX)

It is built with real-time processing in mind and can adapt based on user feedback to improve over time.



Key Features

Text Summarization

  • Extractive Summarization: Identifies and selects the most relevant sentences using TF-IDF and BERT.

  • Abstractive Summarization: Rephrases content into concise and fluent summaries using Google Gemini 1.5.

Image Summarization

  • Uses YOLOv8 for object detection.

  • Employs CLIP for aligning visual content with natural language.

  • Utilizes LLaVA to generate coherent and contextually rich image captions.

Document Summarization

  • Applies OCR tools like Tesseract and Google Vision API to extract text from scanned PDFs and images.

  • NLP models analyze the extracted content to summarize sections such as introductions, key insights, and conclusions.

Real-Time AI Adaptation

  • Incorporates a feedback loop where the system learns from user interactions to enhance future summarization quality.

Multi-format Support

  • Accepts inputs in the form of plain text, image files (JPG, PNG), PDF documents, and Word files (DOCX).

Graphical User Interface

  • Developed using Python’s Tkinter for a clean and interactive user experience.


Technologies and Models Used

The project utilizes a variety of advanced technologies and models across different modules to ensure efficient and accurate summarization and processing. For text summarization, it employs state-of-the-art models like Google Gemini 1.5, BERT, TF-IDF, and Transformer architectures, which collectively enhance the understanding and extraction of key information from textual data. In the domain of image summarization, cutting-edge tools such as YOLOv8, CLIP, and LLaVA are used to analyze and generate concise representations of visual content. Document processing is handled through powerful OCR technologies including Tesseract OCR and the Google Vision API, enabling accurate text extraction from scanned documents and images. The graphical user interface (GUI) is developed using Tkinter in Python, providing a user-friendly and interactive experience. The backend logic is implemented in Python 3.x, ensuring robust and efficient processing capabilities. Additionally, the system incorporates an adaptive feedback learning loop, allowing it to continually improve performance based on user interactions and feedback.






How It Works

1. Text Summarization

  • Extractive approach: Selects key sentences using TF-IDF and BERT.

  • Abstractive approach: Generates human-like summaries using Google Gemini.

2. Image Summarization

  • Detects elements in the image with YOLOv8.

  • Maps visual features to language tokens using CLIP.

  • Creates captions using LLaVA based on scene understanding.

3. Document Summarization

  • Extracts textual data from scanned or digital documents using OCR.

  • Applies NLP algorithms to identify important text segments and summarize them effectively.


Applications

This system has broad applications in sectors such as:

  • Education (e.g., summarizing lecture slides, scanned notes)

  • Healthcare (e.g., summarizing patient reports, medical documents)

  • Legal (e.g., summarizing contracts, case files)

  • Media and Journalism (e.g., summarizing news articles and reports)

  • Research and Academia (e.g., summarizing papers, findings)


Sample Results






About the Author

Gudepu Rakshitha & Natuva Bhavana
B.Tech in Computer Science and Engineering
Alliance University
Email: rakshithareddy1985@gmail.com & bhavanavishwanth02@gmail.com

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