Multimodal OCR: Parse Anything from Documents
Authors: name: Handong Zheng, name: Yumeng Li, name: Kaile Zhang, name: Liang Xin, name: Guangwei Zhao, name: Hao Liu, name: Jiayu Chen, name: Jie Lou, name: Jiyu Qiu, name: Qi Fu, name: Rui Yang, name: Shuo Jiang, name: Weijian Luo, name: Weijie Su, name: Weijun Zhang, name: Xingyu Zhu, name: Yabin Li, name: Yiwei ma, name: Yu Chen, name: Zhaohui Yu, name: Guang Yang, name: Colin Zhang, name: Lei Zhang, name: Yuliang Liu, name: Xiang Bai

We present Multimodal OCR (MOCR), a document parsing paradigm that jointly parses text and graphics into unified textual representations. Unlike conventional OCR systems that focus on text recognition and leave graphical regions as cropped pixels, our method, termed dots.mocr, treats visual elements such as charts, diagrams, tables, and icons as first-class parsing targets, enabling systems to parse documents while preserving semantic relationships across elements. It offers several advantages: (1) it reconstructs both text and graphics as structured outputs, enabling more faithful document reconstruction; (2) it supports end-to-end training over heterogeneous document elements, allowing models to exploit semantic relations between textual and visual components; and (3) it converts previously discarded graphics into reusable code-level supervision, unlocking multimodal supervision embedded in existing documents. To make this paradigm practical at scale, we build a comprehensive data engine from PDFs, rendered webpages, and native SVG assets, and train a compact 3B-parameter model through staged pretraining and supervised fine-tuning. We evaluate dots.mocr from two perspectives: document parsing and structured graphics parsing. On document parsing benchmarks, it ranks second only to Gemini 3 Pro on our OCR Arena Elo leaderboard, surpasses existing open-source document parsing systems, and sets a new state of the art of 83.9 on olmOCR Bench. On structured graphics parsing, dots.mocr achieves higher reconstruction quality than Gemini 3 Pro across image-to-SVG benchmarks, demonstrating strong performance on charts, UI layouts, scientific figures, and chemical diagrams. These results show a scalable path toward building large-scale image-to-code corpora for multimodal pretraining. Code and models are publicly available at https://github.com/rednote-hilab/dots.mocr.