Abstract
This paper presents an automated system for scientific graph detection, classification, and data extraction from technical documents using a computer vision classification pipeline and optical character recognition (OCR). The system processes published research papers by detecting graph regions within PDF documents, isolating plots using an object detection model, and reconstructing numerical datasets from visual data points. Axis labels and tick values are extracted using OCR, while image processing techniques via LineFormer identify plot series data. Testing across 900 graphs achieved a Mean Average Precision (mAP) of 86.6% and a normalized average plot-value reconstruction error of 1.65%. The extracted datasets were applied, in conjunction with technical paper text documentation, through a retrieval-augmented large language model for further research insights. This process demonstrates reliable automated digitization of graphical data from scientific literature and supports the use of large language models for reasoning-based research recommendations.