Automated homework assessment requires not only recognizing student answers but accurately locating complete answers and intermediate reasoning steps in noisy, multi-page handwritten work. We address the missing evaluation setting of *page-aware, two-level answer-region grounding*: given homework page images, a model must localize complete answer regions and their ordered step-level subregions. We introduce **HG-Bench**, comprising 500 human-annotated K–12 homework samples curated from a 1,489,278-image source pool, with hierarchically linked question- and step-level boxes. Its page-aware protocol separately measures complete-answer localization (ℱ_A) and step-level decomposition (ℱ_S^𝜇), distinguishing structured spatial grounding from visible-text parsing. Across frontier closed-source and open-weight VLMs, no zero-shot system exceeds 55.22% on ℱ_A or 48.22% on ℱ_S^𝜇, whereas a GLM-4.6V 9B reference fine-tuned on approximately 10k in-domain examples reaches 74.97/72.26. These results expose step-level handwritten grounding as a concrete capability gap and provide a reproducible benchmark, evaluation protocol, and trained reference for future work on automated homework assessment.