README.md

---
license: apache-2.0
library_name: PaddleOCR
language:
- en
- zh
pipeline_tag: image-to-text
tags:
- OCR
- PaddlePaddle
- PaddleOCR
- layout_detection
---

# PicoDet_layout_1x

## Introduction

A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting 5-Class english document area, including Text, Title, Table, Figure, and List. The key metrics are as follow:

| Model| mAP(0.5) (%) | 
|  --- | --- | 
|PicoDet_layout_1x |  97.8 | 



## 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 layout_detection \
    --model_name PicoDet_layout_1x \
    -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/-oU2IpNLcA0gTMJ34wDBR.png
```

You can also integrate the model inference of the layout detection module into your project. Before running the following code, please download the sample image to your local machine.

```python
from paddleocr import LayoutDetection

model = LayoutDetection(model_name="PicoDet_layout_1x")
output = model.predict("-oU2IpNLcA0gTMJ34wDBR.png", batch_size=1, layout_nms=True)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")
```

After running, the obtained result is as follows:

```json
{'res': {'input_path': '-oU2IpNLcA0gTMJ34wDBR.png', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'Text', 'score': 0.9922339916229248, 'coordinate': [84.18926, 1248.3407, 583.2205, 1582.3639]}, {'cls_id': 0, 'label': 'Text', 'score': 0.991148829460144, 'coordinate': [606.0175, 1209.0277, 1105.669, 1538.559]}, {'cls_id': 0, 'label': 'Text', 'score': 0.989486813545227, 'coordinate': [84.11199, 710.821, 582.4992, 1208.4802]}, {'cls_id': 0, 'label': 'Text', 'score': 0.9823781847953796, 'coordinate': [605.9509, 951.964, 1105.8907, 1089.925]}, {'cls_id': 3, 'label': 'Table', 'score': 0.98123699426651, 'coordinate': [608.91614, 301.1981, 1111.2635, 805.45233]}, {'cls_id': 4, 'label': 'Figure', 'score': 0.971710741519928, 'coordinate': [84.329, 200.35394, 577.5106, 681.766]}, {'cls_id': 0, 'label': 'Text', 'score': 0.950641930103302, 'coordinate': [607.27435, 826.2146, 1107.0017, 911.43726]}, {'cls_id': 0, 'label': 'Text', 'score': 0.9468774795532227, 'coordinate': [605.6758, 197.66481, 1105.3107, 265.3606]}, {'cls_id': 1, 'label': 'Title', 'score': 0.86313396692276, 'coordinate': [606.6188, 1170.0265, 827.1196, 1192.0656]}, {'cls_id': 1, 'label': 'Title', 'score': 0.8427881002426147, 'coordinate': [605.2791, 1131.709, 823.0802, 1152.363]}, {'cls_id': 0, 'label': 'Text', 'score': 0.5990690588951111, 'coordinate': [625.3928, 1558.7576, 849.12946, 1583.5391]}]}}
```

The visualized image is as follows:

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/syzCGwEeqaZ6nqM_u8Qs5.png)

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/layout_detection.html#iii-quick-integration).

### 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 scenJust a few lines of code can experience the inference of the pipelinearios.

#### PP-TableMagic (table_recognition_v2)

The General Table Recognition v2 pipeline (PP-TableMagic) is designed to tackle table recognition tasks, identifying tables in images and outputting them in HTML format. PP-TableMagic includes the following 8 modules:

* Table Structure Recognition Module
* Table Classification Module
* Table Cell Detection Module
* Text Detection Module
* Text Recognition Module
* Layout Region Detection Module (optional)
* Document Image Orientation Classification Module (optional)
* Text Image Unwarping Module (optional)

You can quickly experience the PP-TableMagic pipeline with a single command.

```bash
paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/-oU2IpNLcA0gTMJ34wDBR.png \
    --layout_detection_model_name PicoDet_layout_1x \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --save_path ./output \
    --device gpu:0 

```

You can also integrate the PP-TableMagic pipeline into your project. Before running the following code, please download the sample image to your local machine.

```python
from paddleocr import TableRecognitionPipelineV2

pipeline = TableRecognitionPipelineV2(
    layout_detection_model_name=PicoDet_layout_1x,
    use_doc_orientation_classify=False,   # Use use_doc_orientation_classify to enable/disable document orientation classification model
    use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
    device="gpu:0", # Use device to specify GPU for model inference
    )

output = pipeline.predict("-oU2IpNLcA0gTMJ34wDBR.png")
for res in output:
    res.print() ## Print the predicted structured output
    res.save_to_img("./output/")
    res.save_to_xlsx("./output/")
    res.save_to_html("./output/")
    res.save_to_json("./output/")
```

The default model used in pipeline is `PP-DocLayout-L`, so it is needed that specifing to `PicoDet_layout_1x` by argument `layout_detection_model_name`. And you can also use the local model file by argument `layout_detection_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/main/en/version3.x/pipeline_usage/table_recognition_v2.html#2-quick-start).

## Links

[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr)

[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)