arXiv cs.AI· Sidra Hanif·· 14 小时前AI 评分31
inkGround：评测视觉语言模型对真实场景连笔手写细粒度视觉定位能力的基准
inkGround: Benchmarking Fine-grained Visual Grounding Capabilities of Vision-Language Models for Cursive Handwriting in the Wild
AI 导读
inkGround 是一个面向整页相机拍摄手写的笔画轨迹恢复基准，包含 537 张页面图像（约 18.7k 词、约 163k 笔画），参考轨迹由预训练离线转在线模型生成并经 OCR 一致性过滤与人工核验。
正文 · 原文
Abstract:Human handwriting is produced as an ordered sequence of pen movements, but an offline image keeps only the final ink. Handwriting trajectory recovery aims to restore the ordered stroke points and their pen-up/pen-down transitions. Existing image-trajectory pair datasets are recorded with digitizing tablets under controlled conditions, which limits writer coverage and stylistic diversity. Moreover, there is no benchmark that measures the visual grounding capabilities of general-purpose Vision-Language Models (VLMs) for the stroke trajectory recovery task. In this work, we introduce inkGround, a benchmark for stroke trajectory recovery from full-page, camera-captured handwriting. inkGround pairs 537 page images (~18.7k words, ~163k strokes) with reference trajectories produced by a pretrained offline-to-online model, filtered by OCR agreement, and verified by human raters. Because these references are pseudo-labels that represent one plausible pen trajectory, we also evaluate VLMs on the IAM-OnDB dataset, which provides recorded pen trajectories. We cast the task as text-conditioned point grounding: given a page image and a target word, the model must locate the word and output its ordered stroke points. Outputs are scored by trajectory fidelity (DTW, Chamfer distance), readability of the rendered strokes (Accurate Rate, Correct Rate), and style similarity. We benchmark four open-source and three frontier closed-source VLMs with a shared in-context prompt. Closed-source models, notably Gemini-3.1-Pro, produce legible trajectories close to the reference, and this ranking also holds on IAM-OnDB.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38720 [cs.CV] |
| (or arXiv:2610.38720v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38720 arXiv-issued DOI via DataCite |
Submission history
From: Sidra Hanif [view email]
[v1]
Wed, 28 Oct 2026 19:16:05 UTC (9,248 KB)
来源：arXiv cs.AI · arxiv.org