Smart Document Responder
Year: 2025
Type: article
Abstract: Accessing precise information from unstructuredor image-based documents poses a significant challenge inacademic, legal, and professional domains. Traditionalkeyword-based search systems are often ineffective,leading to time-consuming and incomplete informationretrieval. To address this, the Smart Document Responderintroduces a robust, intelligent solution that combinesOptical Character Recognition (OCR) and advancedNatural Language Processing (NLP) techniques to extractcontextually relevant answers in real time.The system accepts a variety of document formats,including PDF, DOCX, TXT, and scanned images. Forimage-based inputs, Tesseract OCR is used to convertvisuals into machine-readable text, while structureddocuments are directly parsed. The extracted contentundergoes semantic embedding and is processed using theGemini API to interpret natural language queriesaccurately and generate relevant responses.Built with a modular architecture and a clean, responsiveStreamlit interface, the system ensures seamless documentingestion, intuitive query interpretation, and efficient APIcommunication. This approach not only enhances thespeed and accuracy of information retrieval but also offersscalability and ease of maintenance. The project representsa significant advancement in document intelligence byenabling users to interact with complex documents throughsimple questions, delivering fast, accurate, and meaningfulinsights.
Author: Dr.T.Amalraj Victoire, Mrs.M.Vasuki, C.Prasanth
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949847646
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