OCR-Aided NLP for Automated Document Summarization

Abstract

The project titled "Automated Text Summarization from Scanned Documents" aims to develop a system that streamlines the process of extracting key information from scanned documents and generating concise textual summaries. The primary focus is on leveraging Optical Character Recognition (OCR) technology to convert scanned images into machine-readable text, followed by the application of Natural Language Processing (NLP) techniques for effective summarization.

The project involves the design and implementation of an intelligent algorithm that identifies important sentences, extracts key phrases, and summarizes the main ideas present in the scanned documents. Advanced NLP models and neural networks will be explored to enhance the accuracy and efficiency of the summarization process. The system's objective is to provide a time-efficient and accurate means of distilling relevant content from large volumes of scanned documents, thereby facilitating improved accessibility and aiding in efficient document management.

The proposed solution has significant potential applications in various domains such as information retrieval, document categorization, and knowledge management. The successful implementation of this project will contribute to the advancement of automated summarization techniques, offering a valuable tool for individuals and organizations dealing with vast amounts of scanned textual data.