Scholarly documents contain multiple gures representing experimental findings. These gures are generated from data which is not reported anywhere else in the paper. We propose a modular architecture for analyzing such gures. Our architecture consists of the following modules: 1. An ex- tractor for gures and associated metadata ( gure captions and mentions) from PDF documents; 2. A Search engine on the extracted gures and metadata; 3. An image processing module for automated data extraction from the gures and 4. A natural language processing module to understand the semantics of the gure. We discuss the challenges in each step, report an extractor algorithm to extract vector graph- ics from scholarly documents and aspecification algorithm for gures. Our extractor algorithm improves the state of the art by more than 10% and thespecification process is very scalable, yet achieves 85% accuracy. We also describe a semi-automatic system for data extraction from gures which is integrated with our search engine to improve user experience.