Using Computer Vision to Extract Document Structure

Amidst the shift to remote learning due to the Coronavirus pandemic, Frederik Brammer explored using computer vision to enhance the efficiency of school servers overwhelmed by the high volume of video and image data. He aimed to create a custom machine learning model capable of extracting tasks from schoolbook pages, thereby reducing the data size by sending only text instead of large images. Using Roboflow’s comprehensive machine learning pipeline, Brammer addressed challenges like image orientation errors and applied image augmentation techniques to improve the model's generalization across different textbook layouts. He evaluated various cloud services for hosting the model, ultimately finding success with the Scaled-YOLOv4 model, which allowed him to efficiently develop, test, and deploy his project with minimal data science expertise, showcasing the potential of computer vision in optimizing educational resources.