File size: 12,742 Bytes
2fd2a64 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 |
<div align="center">
<img src="imgs/logo.jpg" width="80%" >
</div>
<p align="center">
🤗 <a href="https://github.com/alibaba/Logics-Parsing">GitHub</a>   |   🤖 <a href="https://www.modelscope.cn/studios/Alibaba-DT/Logics-Parsing/summary">Demo</a>   |   📑 <a href="https://arxiv.org/abs/2509.19760">Technical Report</a>
</p>
## Introduction
<div align="center">
<img src="imgs/overview.png" alt="LogicsDocBench 概览" style="width: 800px; height: 250px;">
</div>
<div align="center">
<table style="width: 800px;">
<tr>
<td align="center">
<img src="imgs/report.gif" alt="研报示例">
</td>
<td align="center">
<img src="imgs/chemistry.gif" alt="化学分子式示例">
</td>
<td align="center">
<img src="imgs/paper.gif" alt="论文示例">
</td>
<td align="center">
<img src="imgs/handwritten.gif" alt="手写示例">
</td>
</tr>
<tr>
<td align="center"><b>report</b></td>
<td align="center"><b>chemistry</b></td>
<td align="center"><b>paper</b></td>
<td align="center"><b>handwritten</b></td>
</tr>
</table>
</div>
Logics-Parsing is a powerful, end-to-end document parsing model built upon a general Vision-Language Model (VLM) through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). It excels at accurately analyzing and structuring highly complex documents.
## Key Features
* **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.
## Benchmark
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.
<div align="center">
<img src="imgs/BenchCls.png">
</div>
<table>
<tr>
<td rowspan="2">Model Type</td>
<td rowspan="2">Methods</td>
<td colspan="2">Overall <sup>Edit</sup> ↓</td>
<td colspan="2">Text Edit <sup>Edit</sup> ↓</td>
<td colspan="2">Formula <sup>Edit</sup> ↓</td>
<td colspan="2">Table <sup>TEDS</sup> ↑</td>
<td colspan="2">Table <sup>Edit</sup> ↓</td>
<td colspan="2">ReadOrder<sup>Edit</sup> ↓</td>
<td rowspan="1">Chemistry<sup>Edit</sup> ↓</td>
<td rowspan="1">HandWriting<sup>Edit</sup> ↓</td>
</tr>
<tr>
<td>EN</td>
<td>ZH</td>
<td>EN</td>
<td>ZH</td>
<td>EN</td>
<td>ZH</td>
<td>EN</td>
<td>ZH</td>
<td>EN</td>
<td>ZH</td>
<td>EN</td>
<td>ZH</td>
<td>ALL</td>
<td>ALL</td>
</tr>
<tr>
<td rowspan="7">Pipeline Tools</td>
<td>doc2x</td>
<td>0.209</td>
<td>0.188</td>
<td>0.128</td>
<td>0.194</td>
<td>0.377</td>
<td>0.321</td>
<td>81.1</td>
<td>85.3</td>
<td><ins>0.148</ins></td>
<td><ins>0.115</ins></td>
<td>0.146</td>
<td>0.122</td>
<td>1.0</td>
<td>0.307</td>
</tr>
<tr>
<td>Textin</td>
<td>0.153</td>
<td>0.158</td>
<td>0.132</td>
<td>0.190</td>
<td>0.185</td>
<td>0.223</td>
<td>76.7</td>
<td><ins>86.3</ins></td>
<td>0.176</td>
<td><b>0.113</b></td>
<td><b>0.118</b></td>
<td><b>0.104</b></td>
<td>1.0</td>
<td>0.344</td>
</tr>
<tr>
<td>mathpix<sup>*</sup></td>
<td><ins>0.128</ins></td>
<td><ins>0.146</ins></td>
<td>0.128</td>
<td><ins>0.152</ins></td>
<td><b>0.06</b></td>
<td><b>0.142</b></td>
<td><b>86.2</b></td>
<td><b>86.6</b></td>
<td><b>0.120</b></td>
<td>0.127</td>
<td>0.204</td>
<td>0.164</td>
<td>0.552</td>
<td>0.263</td>
</tr>
<tr>
<td>PP_StructureV3</td>
<td>0.220</td>
<td>0.226</td>
<td>0.172</td>
<td>0.29</td>
<td>0.272</td>
<td>0.276</td>
<td>66</td>
<td>71.5</td>
<td>0.237</td>
<td>0.193</td>
<td>0.201</td>
<td>0.143</td>
<td>1.0</td>
<td>0.382</td>
</tr>
<tr>
<td>Mineru2</td>
<td>0.212</td>
<td>0.245</td>
<td>0.134</td>
<td>0.195</td>
<td>0.280</td>
<td>0.407</td>
<td>67.5</td>
<td>71.8</td>
<td>0.228</td>
<td>0.203</td>
<td>0.205</td>
<td>0.177</td>
<td>1.0</td>
<td>0.387</td>
</tr>
<tr>
<td>Marker</td>
<td>0.324</td>
<td>0.409</td>
<td>0.188</td>
<td>0.289</td>
<td>0.285</td>
<td>0.383</td>
<td>65.5</td>
<td>50.4</td>
<td>0.593</td>
<td>0.702</td>
<td>0.23</td>
<td>0.262</td>
<td>1.0</td>
<td>0.50</td>
</tr>
<tr>
<td>Pix2text</td>
<td>0.447</td>
<td>0.547</td>
<td>0.485</td>
<td>0.577</td>
<td>0.312</td>
<td>0.465</td>
<td>64.7</td>
<td>63.0</td>
<td>0.566</td>
<td>0.613</td>
<td>0.424</td>
<td>0.534</td>
<td>1.0</td>
<td>0.95</td>
</tr>
<tr>
<td rowspan="8">Expert VLMs</td>
<td>Dolphin</td>
<td>0.208</td>
<td>0.256</td>
<td>0.149</td>
<td>0.189</td>
<td>0.334</td>
<td>0.346</td>
<td>72.9</td>
<td>60.1</td>
<td>0.192</td>
<td>0.35</td>
<td>0.160</td>
<td>0.139</td>
<td>0.984</td>
<td>0.433</td>
</tr>
<tr>
<td>dots.ocr</td>
<td>0.186</td>
<td>0.198</td>
<td><ins>0.115</ins></td>
<td>0.169</td>
<td>0.291</td>
<td>0.358</td>
<td>79.5</td>
<td>82.5</td>
<td>0.172</td>
<td>0.141</td>
<td>0.165</td>
<td>0.123</td>
<td>1.0</td>
<td><ins>0.255</ins></td>
</tr>
<tr>
<td>MonkeyOcr</td>
<td>0.193</td>
<td>0.259</td>
<td>0.127</td>
<td>0.236</td>
<td>0.262</td>
<td>0.325</td>
<td>78.4</td>
<td>74.7</td>
<td>0.186</td>
<td>0.294</td>
<td>0.197</td>
<td>0.180</td>
<td>1.0</td>
<td>0.623</td>
</tr>
<tr>
<td>OCRFlux</td>
<td>0.252</td>
<td>0.254</td>
<td>0.134</td>
<td>0.195</td>
<td>0.326</td>
<td>0.405</td>
<td>58.3</td>
<td>70.2</td>
<td>0.358</td>
<td>0.260</td>
<td>0.191</td>
<td>0.156</td>
<td>1.0</td>
<td>0.284</td>
</tr>
<tr>
<td>Gotocr</td>
<td>0.247</td>
<td>0.249</td>
<td>0.181</td>
<td>0.213</td>
<td>0.231</td>
<td>0.318</td>
<td>59.5</td>
<td>74.7</td>
<td>0.38</td>
<td>0.299</td>
<td>0.195</td>
<td>0.164</td>
<td>0.969</td>
<td>0.446</td>
</tr>
<tr>
<td>Olmocr</td>
<td>0.341</td>
<td>0.382</td>
<td>0.125</td>
<td>0.205</td>
<td>0.719</td>
<td>0.766</td>
<td>57.1</td>
<td>56.6</td>
<td>0.327</td>
<td>0.389</td>
<td>0.191</td>
<td>0.169</td>
<td>1.0</td>
<td>0.294</td>
</tr>
<tr>
<td>SmolDocling</td>
<td>0.657</td>
<td>0.895</td>
<td>0.486</td>
<td>0.932</td>
<td>0.859</td>
<td>0.972</td>
<td>18.5</td>
<td>1.5</td>
<td>0.86</td>
<td>0.98</td>
<td>0.413</td>
<td>0.695</td>
<td>1.0</td>
<td>0.927</td>
</tr>
<tr>
<td><b>Logics-Parsing</b></td>
<td><b>0.124</b></td>
<td><b>0.145</b></td>
<td><b>0.089</b></td>
<td><b>0.139</b></td>
<td><ins>0.106</ins></td>
<td><ins>0.165</ins></td>
<td>76.6</td>
<td>79.5</td>
<td>0.165</td>
<td>0.166</td>
<td><ins>0.136</ins></td>
<td><ins>0.113</ins></td>
<td><b>0.519</b></td>
<td><b>0.252</b></td>
</tr>
<tr>
<td rowspan="5">General VLMs</td>
<td>Qwen2VL-72B</td>
<td>0.298</td>
<td>0.342</td>
<td>0.142</td>
<td>0.244</td>
<td>0.431</td>
<td>0.363</td>
<td>64.2</td>
<td>55.5</td>
<td>0.425</td>
<td>0.581</td>
<td>0.193</td>
<td>0.182</td>
<td>0.792</td>
<td>0.359</td>
</tr>
<tr>
<td>Qwen2.5VL-72B</td>
<td>0.233</td>
<td>0.263</td>
<td>0.162</td>
<td>0.24</td>
<td>0.251</td>
<td>0.257</td>
<td>69.6</td>
<td>67</td>
<td>0.313</td>
<td>0.353</td>
<td>0.205</td>
<td>0.204</td>
<td>0.597</td>
<td>0.349</td>
</tr>
<tr>
<td>Doubao-1.6</td>
<td>0.188</td>
<td>0.248</td>
<td>0.129</td>
<td>0.219</td>
<td>0.273</td>
<td>0.336</td>
<td>74.9</td>
<td>69.7</td>
<td>0.180</td>
<td>0.288</td>
<td>0.171</td>
<td>0.148</td>
<td>0.601</td>
<td>0.317</td>
</tr>
<tr>
<td>GPT-5</td>
<td>0.242</td>
<td>0.373</td>
<td>0.119</td>
<td>0.36</td>
<td>0.398</td>
<td>0.456</td>
<td>67.9</td>
<td>55.8</td>
<td>0.26</td>
<td>0.397</td>
<td>0.191</td>
<td>0.28</td>
<td>0.88</td>
<td>0.46</td>
</tr>
<tr>
<td>Gemini2.5 pro</td>
<td>0.185</td>
<td>0.20</td>
<td><ins>0.115</ins></td>
<td>0.155</td>
<td>0.288</td>
<td>0.326</td>
<td><ins>82.6</ins></td>
<td>80.3</td>
<td>0.154</td>
<td>0.182</td>
<td>0.181</td>
<td>0.136</td>
<td><ins>0.535</ins></td>
<td>0.26</td>
</tr>
</table>
<!-- 脚注说明 -->
<tr>
<td colspan="5">
<sup>*</sup> Tested on the v3/PDF Conversion API (August 2025 deployment).
</td>
</tr>
## Quick Start
### 1. Installation
```shell
conda create -n logis-parsing python=3.10
conda activate logis-parsing
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
```
### 2. Download Model Weights
```
# Download our model from Modelscope.
pip install modelscope
python download_model.py -t modelscope
# Download our model from huggingface.
pip install huggingface_hub
python download_model.py -t huggingface
```
### 3. Inference
```shell
python3 inference.py --image_path PATH_TO_INPUT_IMG --output_path PATH_TO_OUTPUT --model_path PATH_TO_MODEL
```
## Acknowledgments
We would like to acknowledge the following open-source projects that provided inspiration and reference for this work:
- [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL)
- [OmniDocBench](https://github.com/opendatalab/OmniDocBench)
- [Mathpix](https://mathpix.com/)
|