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arXiv AI By Sidra Hanif

inkGround: Benchmarking Fine-grained Visual Grounding Capabilities of Vision-Language Models for Cursive Handwriting in the Wild

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inkGround is a new benchmark for evaluating how well Vision‑Language Models can recover the fine‑grained stroke trajectories of cursive handwriting from camera‑captured page images. It contains 537 full‑page images with 18.7k words and 163k strokes, paired with pseudo‑trajectory references generated by an offline‑to‑online model and verified by humans. The benchmark casts the task as text‑conditioned point grounding, scoring models on trajectory fidelity, readability, and style similarity, and compares both open‑source and closed‑source VLMs, showing that closed‑source models like Gemini‑3.1‑Pro perform best.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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