Logics-Parsing

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## Updates * [2026/03/09] We release the [**Logics-Parsing-Omni**](https://github.com/alibaba/Logics-Parsing/tree/main/Logics-Parsing-Omni). For more details, please check our [**Technical Report**](https://arxiv.org/pdf/2603.09677). * [2026/02/13] We release Logics-Parsing-v2 Model. * [2025/09/25] We release Logics-Parsing Model. For more details, please check our [**Technical Report**](https://arxiv.org/pdf/2509.19760). ## Introduction **Logics-Parsing-v2** is an advanced evolution of the previously proposed Logics-Parsing (v1). It inherits all the core capabilities of v1 model, while demonstrating more powerful capabilities on handling complex documents. Furthermore, it extends support for **Parsing-2.0** scenarios, enabling structured parsing of musical sheets, flowcharts, as well as code/pseudocode blocks. ## Key Features v1 * **Effortless End-to-End Processing** * Our single-model architecture eliminates the need for complex, multi-stage pipelines. Deployment and inference are straightforward, going directly from a document image to structured output. * It demonstrates exceptional performance on documents with challenging layouts. * **Advanced Content Recognition** * It accurately recognizes and structures difficult content, including intricate scientific formulas. * Chemical structures are intelligently identified and can be represented in the standard **SMILES** format. * **Rich, Structured HTML Output** * The model generates a clean HTML representation of the document, preserving its logical structure. * Each content block (e.g., paragraph, table, figure, formula) is tagged with its **category**, **bounding box coordinates**, and **OCR text**. * It automatically identifies and filters out irrelevant elements like headers and footers, focusing only on the core content. * **State-of-the-Art Performance** * Logics-Parsing achieves the best performance on our in-house benchmark, which is specifically designed to comprehensively evaluate a model’s parsing capability on complex-layout documents and STEM content. ### v2 * **Effortless End-to-End Processing** * End-to-end recognition and parsing for various kinds of document elements within a single model. * Handles complex-layout and text-dense documents such as newspapers and magazines with exceptional precision and ease; * **Advanced Content Recognition** * Smaller in size, greater in performance, delivering more accurate and structured parsing of tables and scientific formulas. * Introducing **Parsing-2.0**: natively supports parsing of diverse structured content, including flowcharts, music sheets and pseudocode blocks. * **Rich, Structured HTML Output** * Transforms documents into concise HTML -- capturing not just content, but also element types, spatial layouts, and semantic hierarchy. * More scientific and intuitive formats for structured elements -- such as Mermaid for flowcharts and ABC notation for musical scores. * **State-of-the-Art Performance** * SOTA across the board: Logics-Parsing-v2 sets top records on both our in-house benchmark (overall score: **82.16**) and the renowned public benchmark OmniDocBench-v1.5 (overall score: **93.23**). ## Benchmark v1 Existing document-parsing benchmarks often provide limited coverage of complex layouts and STEM content. To address this, we constructed an in-house benchmark comprising 1,078 page-level images across nine major categories and over twenty sub-categories. Our model achieves the best performance on this benchmark. Model Type Methods Overall Edit ↓ Text Edit Edit ↓ Formula Edit ↓ Table TEDS ↑ Table Edit ↓ ReadOrderEdit ↓ ChemistryEdit ↓ HandWritingEdit ↓ EN ZH EN ZH EN ZH EN ZH EN ZH EN ZH ALL ALL Pipeline Tools doc2x 0.209 0.188 0.128 0.194 0.377 0.321 81.1 85.3 0.148 0.115 0.146 0.122 1.0 0.307 Textin 0.153 0.158 0.132 0.190 0.185 0.223 76.7 86.3 0.176 0.113 0.118 0.104 1.0 0.344 mathpix* 0.128 0.146 0.128 0.152 0.06 0.142 86.2 86.6 0.120 0.127 0.204 0.164 0.552 0.263 PP_StructureV3 0.220 0.226 0.172 0.29 0.272 0.276 66 71.5 0.237 0.193 0.201 0.143 1.0 0.382 Mineru2 0.212 0.245 0.134 0.195 0.280 0.407 67.5 71.8 0.228 0.203 0.205 0.177 1.0 0.387 Marker 0.324 0.409 0.188 0.289 0.285 0.383 65.5 50.4 0.593 0.702 0.23 0.262 1.0 0.50 Pix2text 0.447 0.547 0.485 0.577 0.312 0.465 64.7 63.0 0.566 0.613 0.424 0.534 1.0 0.95 Expert VLMs Dolphin 0.208 0.256 0.149 0.189 0.334 0.346 72.9 60.1 0.192 0.35 0.160 0.139 0.984 0.433 dots.ocr 0.186 0.198 0.115 0.169 0.291 0.358 79.5 82.5 0.172 0.141 0.165 0.123 1.0 0.255 MonkeyOcr 0.193 0.259 0.127 0.236 0.262 0.325 78.4 74.7 0.186 0.294 0.197 0.180 1.0 0.623 OCRFlux 0.252 0.254 0.134 0.195 0.326 0.405 58.3 70.2 0.358 0.260 0.191 0.156 1.0 0.284 Gotocr 0.247 0.249 0.181 0.213 0.231 0.318 59.5 74.7 0.38 0.299 0.195 0.164 0.969 0.446 Olmocr 0.341 0.382 0.125 0.205 0.719 0.766 57.1 56.6 0.327 0.389 0.191 0.169 1.0 0.294 SmolDocling 0.657 0.895 0.486 0.932 0.859 0.972 18.5 1.5 0.86 0.98 0.413 0.695 1.0 0.927 Logics-Parsing 0.124 0.145 0.089 0.139 0.106 0.165 76.6 79.5 0.165 0.166 0.136 0.113 0.519 0.252 General VLMs Qwen2VL-72B 0.298