Vision-Language Models (VLMs) have demonstrated remarkable performance in document understanding tasks; however, VLMs also suffer from hallucinations inherited from LLMs. While prior work has focused on reasoning-stage hallucinations, the role of visual perception remains underexplored. In this work, we define perceptual hallucination as the phenomenon where VLMs generate information as if perceived, despite absent or damaged visual evidence. To analyze this, we construct DocHallu, a benchmark of 2,671 original–damaged image pairs across three tasks, available at https://huggingface.co/datasets/IB99/DocHallu. Experiments reveal that perceptual hallucination occurs across all models, with higher rates for numerical content than textual content. Activation patching analysis suggests that hallucinations are strongly associated with errors introduced in the vision encoder, which can subsequently propagate and become amplified through the text decoding process. We also demonstrate that LLM-based post-hoc filtering can reduce hallucination exposure by 36% on average, with reductions of up to 88%. This work extends VLM hallucination research by defining, analyzing, and verifying perceptual hallucination in document understanding.