Vision-language models (VLMs) often produce details not supported by the input image. The SHROOM-Visions 2026 shared task studies this problem at the character level: given an image, a question, and a VLM-generated answer, systems must identify the hallucinated spans and assign an error type to each one. Our approach fine-tunes Qwen2.5-VL-7B and adds two lightweight classification heads to predict the hallucination probability and error type of each answer token. We map these predictions back to characters, decode spans aligned with word boundaries, and combine them with complementary components for selected languages. These include a fine-tuned InternVL3.5 model and a claim verification pipeline in which the base Qwen2.5-VL checks answer claims against the image. Across the 4 languages, our submissions reached an average Spearman correlation of 0.47 with human annotations, or 0.36 when the predicted error type must also be correct. Our best result is obtained on Italian, where combining the two fine-tuned models reaches 0.50 correlation, ranking sixth on the official leaderboard.