# Introduction to PP-StructureV3 **PP-StructureV3** pipeline, based on the Layout Parsing v1 pipeline, has strengthened the ability of layout detection, table recognition, and formula recognition. It has also added the ability to understand charts and restore reading order, as well as the ability to convert results into Markdown files. In various document data, it performs excellently and can handle more complex document data. This pipeline also provides flexible service-oriented deployment methods, supporting the use of multiple programming languages on various hardware. Moreover, it also provides the ability for secondary development. You can train and optimize on your own dataset based on this pipeline, and the trained model can be seamlessly integrated.
# Key Metrics
Method Type Methods OverallEdit↓ TextEdit↓ FormulaEdit↓ TableEdit↓ Read OrderEdit↓
EN ZH EN ZH EN ZH EN ZH EN ZH
Pipeline Tools PP-structureV3 0.145 0.206 0.058 0.088 0.295 0.535 0.159 0.109 0.069 0.091
MinerU-0.9.3 0.15 0.357 0.061 0.215 0.278 0.577 0.18 0.344 0.079 0.292
MinerU-1.3.11 0.166 0.310 0.0826 0.2000 0.3368 0.6236 0.1613 0.1833 0.0834 0.2316
Marker-1.2.3 0.336 0.556 0.08 0.315 0.53 0.883 0.619 0.685 0.114 0.34
Mathpix 0.191 0.365 0.105 0.384 0.306 0.454 0.243 0.32 0.108 0.304
Docling-2.14.0 0.589 0.909 0.416 0.987 0.999 1 0.627 0.81 0.313 0.837
Pix2Text-1.1.2.3 0.32 0.528 0.138 0.356 0.276 0.611 0.584 0.645 0.281 0.499
Unstructured-0.17.2 0.586 0.716 0.198 0.481 0.999 1 1 0.998 0.145 0.387
OpenParse-0.7.0 0.646 0.814 0.681 0.974 0.996 1 0.284 0.639 0.595 0.641
Expert VLMs GOT-OCR 0.287 0.411 0.189 0.315 0.36 0.528 0.459 0.52 0.141 0.28
Nougat 0.452 0.973 0.365 0.998 0.488 0.941 0.572 1 0.382 0.954
Mistral OCR 0.268 0.439 0.072 0.325 0.318 0.495 0.6 0.65 0.083 0.284
OLMOCR-sglang 0.326 0.469 0.097 0.293 0.455 0.655 0.608 0.652 0.145 0.277
SmolDocling-256M_transformer 0.493 0.816 0.262 0.838 0.753 0.997 0.729 0.907 0.227 0.522
General VLMs Gemini2.0-flash 0.191 0.264 0.091 0.139 0.389 0.584 0.193 0.206 0.092 0.128
Gemini2.5-Pro 0.148 0.212 0.055 0.168 0.356 0.439 0.13 0.119 0.049 0.121
GPT4o 0.233 0.399 0.144 0.409 0.425 0.606 0.234 0.329 0.128 0.251
Qwen2-VL-72B 0.252 0.327 0.096 0.218 0.404 0.487 0.387 0.408 0.119 0.193
Qwen2.5-VL-72B 0.214 0.261 0.092 0.18 0.315 0.434 0.341 0.262 0.106 0.168
InternVL2-76B 0.44 0.443 0.353 0.29 0.543 0.701 0.547 0.555 0.317 0.228
The above data is from: * OmniDocBench * OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations # End to End Benchmark The performance of PP-StructureV3 and MinerU with different configurations under different GPU environments are as follows. Requirements: * Paddle 3.0 * PaddleOCR 3.0.0 * MinerU 1.3.10 * CUDA 11.8 * cuDNN 8.9 ## Local inference Local inference was tested with both V100 and A100 GPU, evaluating the performance of PP-StructureV3 under 6 different configurations. The test data consists of 15 PDF files, totaling 925 pages, including elements such as tables, formulas, seals, and charts. In the following PP-StructureV3 configuration, please refer to [PP-OCRv5](../PP-OCRv5/PP-OCRv5.en.md) for OCR model details, see [Formula Recognition](../../module_usage/formula_recognition.en.md) for formula recognition model details, and refer to [Text Detection](../../module_usage/text_detection.en.md) for the max_side_limit setting of the text detection module. ### Env: NVIDIA Tesla V100 + Intel Xeon Gold 6271C
Methods Configurations Average time per page (s) Average CPU （%） Peak RAM Usage （GB） Average RAM Usage （GB） Average GPU （%） Peak VRAM Usage （GB） Average VRAM Usage （GB）
PP-StructureV3 OCR Models Formula Recognition Model Chart Recognition Model text detection module max_side_limit
Server PP-FormulaNet-L ✗ 4096 1.77 111.4 6.7 5.2 38.9 17.0 16.5
Server PP-FormulaNet-L ✔ 4096 4.09 105.3 5.5 4.0 24.7 17.0 16.6
Mobile PP-FormulaNet-L ✗ 4096 1.56 113.7 6.6 4.9 29.1 10.7 10.6
Server PP-FormulaNet-M ✗ 4096 1.42 112.9 6.8 5.1 38 16.0 15.5
Mobile PP-FormulaNet-M ✗ 4096 1.15 114.8 6.5 5.0 26.1 8.4 8.3
Mobile PP-FormulaNet-M ✗ 1200 0.99 113 7.0 5.6 29.2 8.6 8.5
MinerU - 1.57 142.9 13.3 11.8 43.3 31.6 9.7
### NVIDIA A100 + Intel Xeon Platinum 8350C
Methods Configurations Average time per page (s) Average CPU （%） Peak RAM Usage （GB） Average RAM Usage （GB） Average GPU （%） Peak VRAM Usage （GB） Average VRAM Usage （GB）
PP-StructureV3 OCR Models Formula Recognition Model Chart Recognition Model text detection module max_side_limit
Server PP-FormulaNet-L ✗ 4096 1.12 109.8 9.2 7.8 29.8 21.8 21.1
Server PP-FormulaNet-L ✔ 4096 2.76 103.7 9.0 7.7 24 21.8 21.1
Mobile PP-FormulaNet-L ✗ 4096 1.04 110.7 9.3 7.8 22 12.2 12.1
Server PP-FormulaNet-M ✗ 4096 0.95 111.4 9.1 7.8 28.1 21.8 21.0
Mobile PP-FormulaNet-M ✗ 4096 0.89 112.1 9.2 7.8 18.5 11.4 11.2
Mobile PP-FormulaNet-M ✗ 1200 0.64 113.5 10.2 8.5 23.7 11.4 11.2
MinerU - 1.06 168.3 18.3 16.8 27.5 76.9 14.8
## Serving Inference The serving inference test is based on the NVIDIA A100 + Intel Xeon Platinum 8350C environment, with test data consisting of 1500 images, including tables, formulas, seals, charts, and other elements.
Instances Number Concurrent Requests Number Throughput Average Latency (s) Success Number/Total Number
4 GPUs ✖️ 1 instance/gpu 4 1.69 2.36 100%
4 GPUs ✖️ 4 instances/gpu 16 4.05 3.87 100%
## Pipeline benchmark data
Click to expand/collapse the table
Pipeline configurationHardwareAvg. inference time (s)Peak CPU utilization (%)Avg. CPU utilization (%)Peak host memory (MB)Avg. host memory (MB)Peak GPU utilization (%)Avg. GPU utilization (%)Peak device memory (MB)Avg. device memory (MB)
PP_StructureV3-default Intel 8350C + A100 1.38 1384.60 113.26 5781.59 3431.21 100 32.79 37370.00 34165.68
Intel 6271C + V100 2.38 608.70 109.96 6388.91 3737.19 100 39.08 26824.00 24581.61
Intel 8563C + H20 1.36 744.30 112.82 6199.01 3865.78 100 43.81 35132.00 32077.12
Intel 8350C + A10 1.74 418.50 105.96 6138.25 3503.41 100 48.54 18536.00 18353.93
Intel 6271C + T4 3.70 434.40 105.45 6865.87 3595.68 100 71.92 13970.00 12668.58
PP_StructureV3-pp Intel 8350C + A100 3.50 679.30 105.96 13850.20 5146.50 100 14.01 37656.00 34716.95
Intel 6271C + V100 5.03 494.20 105.63 13542.94 4833.55 100 20.36 29402.00 26607.92
Intel 8563C + H20 3.17 481.50 105.13 14179.97 5608.80 100 19.35 35454.00 32512.19
PP_StructureV3-full Intel 8350C + A100 8.92 697.30 102.88 13777.07 4573.65 100 18.39 38776.00 37554.09
Intel 6271C + V100 13.12 437.40 102.36 13974.00 4484.00 100 17.50 29878.00 28733.59
PP_StructureV3-seal Intel 8350C + A100 1.39 747.50 112.55 5788.79 3742.03 100 33.81 38966.00 35832.44
Intel 6271C + V100 2.44 630.10 110.18 6343.39 3725.98 100 42.23 28078.00 25834.70
Intel 8563C + H20 1.40 792.20 113.63 6673.60 4417.34 100 46.33 35530.00 32516.87
Intel 8350C + A10 1.75 422.40 106.08 6068.87 3973.49 100 50.12 19630.00 18374.37
Intel 6271C + T4 3.76 400.30 105.10 6296.28 3651.42 100 72.57 14304.00 13268.36
PP_StructureV3-chart Intel 8350C + A100 7.70 746.80 102.69 6355.58 4006.48 100 22.38 37380.00 36730.73
Intel 6271C + V100 10.58 599.20 102.51 5754.14 3333.78 100 21.99 26820.00 26253.70
Intel 8350C + A10 8.03 413.30 101.31 6473.29 3689.84 100 26.19 18540.00 18494.69
Intel 6271C + T4 11.69 460.90 101.85 6503.12 3524.06 100 46.81 13966.00 12481.94
PP_StructureV3-notable Intel 8350C + A100 1.24 738.30 110.45 5638.16 3278.30 100 35.32 30320.00 27026.17
Intel 6271C + V100 2.24 452.40 107.79 5579.15 3635.95 100 43.00 23098.00 20684.43
Intel 8563C + H20 1.18 989.00 107.71 6041.76 4024.76 100 50.67 33780.00 29733.15
Intel 8350C + A10 1.58 225.00 102.56 5518.10 3333.08 100 49.90 21532.00 18567.99
Intel 6271C + T4 3.40 413.30 103.58 5874.88 3662.49 100 76.82 13764.00 11890.62
PP_StructureV3-noformula Intel 6271C 7.85 1172.50 964.70 17739.00 11101.02 N/A N/A N/A N/A
Intel 8350C 8.83 1053.50 970.64 15463.48 9408.19 N/A N/A N/A N/A
Intel 8350C + A100 0.84 788.60 124.25 6246.39 3674.32 100 30.57 40084.00 37358.45
Intel 6271C + V100 1.42 606.20 115.53 7015.57 3707.03 100 35.63 29540.00 27620.28
Intel 8563C + H20 0.87 644.10 119.23 6895.76 4222.85 100 50.00 36878.00 34104.59
Intel 8350C + A10 1.03 377.50 106.87 5819.88 3830.19 100 42.87 19340.00 17550.94
Intel 6271C + T4 2.02 430.20 109.21 6600.62 3824.18 100 65.75 14332.00 12712.18
PP_StructureV3-lightweight Intel 6271C 4.36 1189.70 995.78 14000.50 9374.97 N/A N/A N/A N/A
Intel 8350C 3.74 1049.60 967.77 12960.96 7644.25 N/A N/A N/A N/A
Hygon 7490 + P800 0.86 572.20 120.84 8290.49 3569.44 N/A N/A N/A N/A
Intel 8350C + A100 0.61 823.40 126.25 9258.22 3776.63 52 18.95 7456.00 7131.95
Intel 6271C + V100 1.07 686.80 116.70 9381.75 4126.28 58 22.92 8450.00 8083.30
Intel 8563C + H20 0.46 999.00 122.21 9734.78 4516.40 61 24.41 7524.00 7167.52
Intel 8350C + A10 0.70 355.40 111.51 9415.45 4094.06 89 30.85 7248.00 6927.58
M4 12.22 223.60 107.35 9531.22 7884.61 N/A N/A N/A N/A
Intel 6271C + T4 1.13 461.40 112.16 7923.09 3837.31 85 41.67 8218.00 7902.04
Pipeline configurationdescription
PP_StructureV3-default Default configuration
PP_StructureV3-pp Based on the default configuration, document image preprocessing is enabled
PP_StructureV3-full Based on the default configuration, document image preprocessing and chart parsing are enabled
PP_StructureV3-seal Based on the default configuration, seal text recognition is enabled
PP_StructureV3-chart Based on the default configuration, chart parsing is enabled
PP_StructureV3-notable Based on the default configuration, table recognition is disabled
PP_StructureV3-noformula Based on the default configuration, formula recognition is disabled
PP_StructureV3-lightweight Based on the default configuration, all task models are replaced with lightweight versions
* Test environment: * PaddlePaddle 3.1.0、CUDA 11.8、cuDNN 8.9 * PaddleX @ develop (f1eb28e23cfa54ce3e9234d2e61fcb87c93cf407) * Docker image: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddle:3.1.0-gpu-cuda11.8-cudnn8.9 * Test data: * Test data containing 280 images including tables, seals, formulas, and charts. * Test strategy: * Warm up with 20 samples, then repeat the full dataset once for performance testing. * Note: * Since we did not collect device memory data for NPU and XPU, the corresponding entries in the table are marked as N/A. # PP-StructureV3 Demo
More Demos # FAQ 1. What is the default configuration? How to get higher accuracy, faster speed, or smaller GPU memory? When using mobile OCR models + PP-FormulaNet_plus-M, and max length of text detection set to 1200, if set use_chart_recognition to False and dont not load the chart recognition model, the GPU memory would be reduced. On the V100, the peak and average GPU memory would be reduced from 8776.0 MB and 8680.8 MB to 6118.0 MB and 6016.7 MB, respectively; On the A100, the peak and average GPU memory would be reduced from 11716.0 MB and 11453.9 MB to 9850.0 MB and 9593.5 MB, respectively. You can using multi-gpus by setting `device` to `gpu:,`, such as `gpu:0,1,2,3`. And about multi-process parallel inference, you can refer: [Multi-Process Parallel Inference](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/pipeline_usage/instructions/parallel_inference.en.md#example-of-multi-process-parallel-inference). 2. About serving deployment (1) Can the service handle requests concurrently? For the basic serving deployment solution, the service processes only one request at a time. This plan is mainly used for rapid verification, to establish the development chain, or for scenarios where concurrent requests are not required. For high-stability serving deployment solution, the service process only one request at a time by default, but you can refer to the related docs to adjust achieve scaling. （2）How to reduce latency and improve throughput? Use the High-performance inference plugin, and deploy multi instances.