dots.mocr 🖥️ Live Demo 💬 WeChat 📕 rednote 🐦 X Introduction We present dots.mocr. Beyond achieving state of the art (SOTA) performance in standard multilingual document parsing among models of comparable size, dots.mocr excels at converting structured graphics (e.g., charts, UI layouts, scientific figures and etc.) directly into SVG code. Its core capabilities encompass grounding, recognition, semantic understanding, and interactive dialogue. Simultaneously, we are releasing dots.mocr svg, a variant specifically optimized for robust image to SVG parsing tasks. More information can be found in the paper. Evaluation 1. Document Parsing 1.1 Elo Score of different bench between latest models models olmOCR Bench OmniDocBench (v1.5) XDocParse Average MonkeyOCR pro 3B 895.0 811.3 637.1 781.1 GLM OCR 884.2 972.6 820.7 892.5 PaddleOCR VL 1.5 897.3 997.9 866.4 920.5 HuanyuanOCR 997.6 1003.9 951.1 984.2 dots.ocr 1041.1 1027.2 1190.3 1086.2 dots.mocr 1104.4 1059.0 1210.7 1124.7 Gemini 3 Pro 1180.4 1128.0 1323.7 1210.7 Notes: Results for Gemini 3 Pro, PaddleOCR VL 1.5, and GLM OCR were obtained via APIs, while HuanyuanOCR results were generated using local inference. The Elo score evaluation…
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