DocReader is an advanced document reading system designed to replace manual document reading processes. It features a modular and scalable architecture, incorporating a chatbot for user interactions, a document reader for text extraction, an image classifier for categorizing user-uploaded images, and an object detector for identifying relevant objects in images. Built on the Streamlit framework, it ensures a seamless user interface and experience. The system leverages external APIs for natural language processing and image processing, aiming to enhance accessibility and efficiency for individuals, businesses, and educational institutions.
- Chatbot Component: Manages user interactions, responds to text inputs, and contributes to the narrative flow.
- Document Reader Component: Processes text documents, extracts information, and integrates relevant content into the narrative.
- Image Classifier Component: Categorizes user-uploaded images to enhance the visual aspect of the storyline.
- Object Detector Component: Identifies and includes relevant objects within user-provided images to contribute to the immersive narrative.
- Streamlit Interface: Acts as the user interface, capturing inputs and displaying outputs cohesively.
- Natural Language Processing (NLP): Implement NLP techniques to convert user-provided text into a machine-readable format for chatbot interactions. This ensures accurate interpretation and extraction of information from text documents.
- Image Classification: Utilize precise image classification algorithms to categorize images provided by users, enhancing the storytelling experience.
- Object Detection: Implement robust object detection algorithms to identify and incorporate relevant objects within user-provided images, adding depth to the narrative.