WildHandBench: A Benchmark for Handwritten Text Understanding that Challenges MLLMs and Humans

• URL: http://arxiv.org/abs/2608.22959v1
• Date: Mon, 24 Aug 2026 08:25:12 GMT
• Title: WildHandBench: A Benchmark for Handwritten Text Understanding that Challenges MLLMs and Humans
• Abstract summary: We present a benchmark containing 500 handwritten documents across three structures (free text, tables, formulas), four languages, and nine real-world scenarios.<n>We introduce a Prior-Driven Error (PDE) metric that quantifies whether errors originate from language priors rather than visual evidence.
• Score: 5.993812625110576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the top model on OmniDocBench now reaches 96.34% overall on printed-document parsing, the ability of current models to handle challenging handwritten documents remains largely uncharacterized. Existing benchmarks focus on isolated text or formulas, overlook handwritten tables and real-world degradation, and report aggregate accuracy without explaining why models fail. We present WildHandBench, a benchmark containing 500 handwritten documents across three structures (free text, tables, formulas), four languages, and nine real-world scenarios. We introduce a Prior-Driven Error (PDE) metric that quantifies whether errors originate from language priors rather than visual evidence. Evaluating 18 state-of-the-art models together with calibrated human baselines, we find: (1) the best model achieves only 71.85% overall; (2) humans outperform all models yet the gap is narrow (77.09% vs. 71.85%); and (3) model errors are qualitatively different from human errors -- 63-91% of model errors are prior-driven versus only 49% for humans, exposing systematic reliance on language priors that conventional accuracy metrics cannot capture.

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