Table and Figure Understanding with Computer Vision

A project described by Timothy M aims to develop a document understanding system using computer vision to automatically retrieve and process information from documents, focusing specifically on tables and figures. The project employs the Table and Figure Identification API built with Roboflow to detect and extract these elements, which are then analyzed using a Vision-Language Model (VLM) to generate detailed explanations. The system's workflow is constructed using Roboflow Workflows, a low-code computer vision application builder, and Gradio framework for designing the user interface. The project involves a series of steps including dataset collection, training a computer vision model, and creating a workflow application that integrates the Roboflow-trained object detection model with OpenAI's GPT-4o API to provide descriptions of identified tables and figures. The final application allows users to upload document images and receive detailed explanations of the content, illustrating the integration of AI and computer vision to enhance document interpretation.