File size: 8,405 Bytes
9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c e60addc 9d62e6c |
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 |
---
license: apache-2.0
library_name: PaddleOCR
language:
- en
- zh
pipeline_tag: image-to-text
tags:
- OCR
- PaddlePaddle
- PaddleOCR
---
# PP-DocBee-2B
## Introduction
The PaddleOCR team has developed PP-DocBee-2B, a multimodal large model focusing on document understanding, and it performs excellently in Chinese document understanding tasks. The model is fine-tuned and optimized using nearly 5 million multimodal datasets for document understanding, including general VQA, OCR, charts, text-rich documents, mathematics and complex reasoning, synthetic data, and pure text data, with different training data ratios set. On several authoritative English document understanding evaluation lists in academia, PP-DocBee has basically achieved SOTA for models of the same parameter scale. In terms of internal business Chinese scenario indicators, PP-DocBee also outperforms the current popular open-source and closed-source models. The key accuracy metrics are as follow:
| Model | Model Storage Size(GB) | Total Score |
|-------|--------------------------|-----------|
| **PP-DocBee-2B** | 4.2 | 765 |
| PP-DocBee-7B | 15.8 | - |
**Note**: The total scores of the above models are test results from an internal evaluation set, where all images have a resolution (height, width) of (1680, 1204), with a total of 1196 data entries, covering scenarios such as financial reports, laws and regulations, scientific and technical papers, manuals, humanities papers, contracts, research reports, etc. There are no plans for public release at the moment.
## Quick Start
### Installation
1. PaddlePaddle
Please refer to the following commands to install PaddlePaddle using pip:
```bash
# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
# for CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
# for CPU
python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
```
For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick).
2. PaddleOCR
Install the latest version of the PaddleOCR inference package from PyPI:
```bash
python -m pip install paddleocr
```
### Model Usage
You can quickly experience the functionality with a single command:
```bash
paddleocr doc_vlm \
--model_name PP-DocBee-2B \
-i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}"
```
You can also integrate the model inference of the document visual-language module into your project. Before running the following code, please download the sample image to your local machine.
```python
from paddleocr import DocVLM
model = DocVLM(model_name="PP-DocBee-2B")
results = model.predict(
input={
"image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png",
"query": "Recognize the content of this table and output it in markdown format."
},
batch_size=1
)
for res in results:
res.print()
res.save_to_json(f"./output/res.json")
```
After running, the obtained result is as follows:
```bash
{'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}}
```
The visualized result is as follows:
```bash
| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |
|---|---|---|---|---|---|
| 1 | China (CHN) | 48 | 22 | 30 | 100 |
| 2 | United States (USA) | 36 | 39 | 37 | 112 |
| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |
| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |
| 5 | Germany (GER) | 16 | 11 | 14 | 41 |
| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |
| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |
| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |
| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |
| 10 | France (FRA) | 7 | 16 | 20 | 43 |
| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |
| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |
| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |
| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |
| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |
```
For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/doc_vlm.html#iii-quick-start).
### Pipeline Usage
The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.
#### doc_understanding
The document understanding pipeline is an advanced document processing technology based on Visual-Language Models (VLM), designed to overcome the limitations of traditional document processing. And there is only 1 module in the pipeline:
* Document Visual Language Module
Run a single command to quickly experience the OCR pipeline:
```bash
paddleocr doc_understanding -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}"
```
Results are printed to the terminal:
```bash
{'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}}
```
If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below:
The command-line method is for quick experience. For project integration, also only a few codes are needed as well:
```python
from paddleocr import DocUnderstanding
pipeline = DocUnderstanding(
doc_understanding_model_name="PP-DocBee-2B"
)
output = pipeline.predict(
{
"image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png",
"query": "Recognize the content of this table and output it in markdown format."
}
)
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_json("./output/")
```
The default model used in pipeline is `PP-DocBee2-3B`, so you need to specify `doc_understanding_model_name` to `PP-DocBee-2B`. And you can also use the local model file by argument `doc_understanding_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/doc_understanding.html#2-quick-start).
## Links
[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr)
[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)
|