Education is the cornerstone of societal progress, yet automated document layout understanding in the education domain remains significantly under-explored, with most research focusing on individual components like texts, tables, images instead of a holistic understanding. Despite the increasing demand for AI-driven assessment, digitization, and retrieval of educational resources, very few dedicated works exists for question paper layout analysis, a critical component of automated learning systems. To bridge this gap, we introduce the first dataset HiLEx, explicitly designed for structure analysis and layout extraction from question paper images. The HiLEx dataset is curated from eight diverse examination formats. With over 1900 annotations with different structural layouts, covering both single-column and multi-column layouts, ensure robust generalization across different structural variations. We conduct a thorough empirical study with most contemporary object detection models, exposing their limitations in structural understanding, and format generalization. Our findings lay the groundwork for Smart AI solutions in education, fostering automated grading, question retrieval, and equitable learning access. This research aligns with UN Sustainable Development Goals (SDG 4: Quality Education, SDG 10: Reduced Inequalities) by enabling scalable, AI-driven assessment technologies, promoting inclusivity, and revolutionizing educational accessibility worldwide. The HiLEx dataset is publicly available in Github ( https://github.com/HiLEx-DLA/HiLEx ).