How to Use GPT-4 To Extract Handwritten Text from Images

Nathan Y.'s guide outlines the process of creating, training, and deploying a custom computer vision workflow using OpenAI's GPT-4 and Roboflow, specifically focusing on digitizing handwriting from images. The guide is structured into three primary steps: building the model, integrating it into a workflow, and deploying it. In the first step, users can either copy an existing model or create their own using Roboflow's extensive model library and datasets. The second step involves adding the trained model to a workflow, incorporating an object detection model and a language model (LMM), and specifying prompts to extract specific information like names and completion times from images. Finally, the deployment step provides a practical example using Python code to process images of homework booklets, showcasing how to extract and organize handwritten information into a digital format. The guide aims to empower users to automate the extraction of handwritten text, facilitating broader applications in document digitization.