OvisOCR3 is out, still based on Qwen3.5-0.8B and available under Apache 2.0, with vLLM inference examples. Compared with OvisOCR2’s Markdown parsing, it adds a few useful options: Structured JSON output with bounding boxes, element categories, and content, plus a layout-only mode. Key information extraction using specified fields or a supplied JSON template. Figure descriptions and Mermaid output for flowcharts. Cross-page table reconstruction: parse the pages first, then use the same model to merge table fragments across adjacent pages. OvisOCR3 results The model card reports 96.74 on OmniDocBench v1.6, 88.80 on MDPBench, and 82.31 on PureDocBench. The bigger reported gains over OvisOCR2 are on MDPBench (69.00 → 88.80) and PureDocBench (75.10 → 82.31). The bounding-box output and cross-page tables seem particularly useful for document workflows. These are the team’s reported numbers, though; I’d be interested in how well the structure holds up on messy scans and long financial tables. Has anyone tried it locally or compared it with GLM-OCR, PaddleOCR-VL, or MinerU? Curious about VRAM usage and processing speed too. HF model card：https://huggingface.co/ATH-MaaS/OvisOCR3   submitted by   /u/Sad_External6106 [link]   [comments]