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# Introduction to PP-OCRv5
**PP-OCRv5** is the new generation text recognition solution of PP-OCR, focusing on multi-scenario and multi-text type recognition. In terms of text types, PP-OCRv5 supports 5 major mainstream text types: Simplified Chinese, Chinese Pinyin, Traditional Chinese, English, and Japanese. For scenarios, PP-OCRv5 has upgraded recognition capabilities for challenging scenarios such as complex Chinese and English handwriting, vertical text, and uncommon characters. On internal complex evaluation sets across multiple scenarios, PP-OCRv5 achieved a 13 percentage point end-to-end improvement over PP-OCRv4.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-OCRv5/algorithm_ppocrv5.png" width="600"/>
</div>
# Key Metrics
### 1. Text Detection Metrics
<table>
<thead>
<tr>
<th>Model</th>
<th>Handwritten Chinese</th>
<th>Handwritten English</th>
<th>Printed Chinese</th>
<th>Printed English</th>
<th>Traditional Chinese</th>
<th>Ancient Text</th>
<th>Japanese</th>
<th>General Scenario</th>
<th>Pinyin</th>
<th>Rotation</th>
<th>Distortion</th>
<th>Artistic Text</th>
<th>Average</th>
</tr>
</thead>
<tbody>
<tr>
<td><b>PP-OCRv5_server_det</b></td>
<td><b>0.803</b></td>
<td><b>0.841</b></td>
<td><b>0.945</b></td>
<td><b>0.917</b></td>
<td><b>0.815</b></td>
<td><b>0.676</b></td>
<td><b>0.772</b></td>
<td><b>0.797</b></td>
<td><b>0.671</b></td>
<td><b>0.8</b></td>
<td><b>0.876</b></td>
<td><b>0.673</b></td>
<td><b>0.827</b></td>
</tr>
<tr>
<td>PP-OCRv4_server_det</td>
<td>0.706</td>
<td>0.249</td>
<td>0.888</td>
<td>0.690</td>
<td>0.759</td>
<td>0.473</td>
<td>0.685</td>
<td>0.715</td>
<td>0.542</td>
<td>0.366</td>
<td>0.775</td>
<td>0.583</td>
<td>0.662</td>
</tr>
<tr>
<td><b>PP-OCRv5_mobile_det</b></td>
<td><b>0.744</b></td>
<td><b>0.777</b></td>
<td><b>0.905</b></td>
<td><b>0.910</b></td>
<td><b>0.823</b></td>
<td><b>0.581</b></td>
<td><b>0.727</b></td>
<td><b>0.721</b></td>
<td><b>0.575</b></td>
<td><b>0.647</b></td>
<td><b>0.827</b></td>
<td>0.525</td>
<td><b>0.770</b></td>
</tr>
<tr>
<td>PP-OCRv4_mobile_det</td>
<td>0.583</td>
<td>0.369</td>
<td>0.872</td>
<td>0.773</td>
<td>0.663</td>
<td>0.231</td>
<td>0.634</td>
<td>0.710</td>
<td>0.430</td>
<td>0.299</td>
<td>0.715</td>
<td><b>0.549</b></td>
<td>0.624</td>
</tr>
</tbody>
</table>
Compared to PP-OCRv4, PP-OCRv5 shows significant improvement in all detection scenarios, especially in handwriting, ancient texts, and Japanese detection capabilities.
### 2. Text Recognition Metrics
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-OCRv5/ocrv5_rec_acc.png" width="600"/>
</div>
<table>
<thead>
<tr>
<th>Evaluation Set Category</th>
<th>Handwritten Chinese</th>
<th>Handwritten English</th>
<th>Printed Chinese</th>
<th>Printed English</th>
<th>Traditional Chinese</th>
<th>Ancient Text</th>
<th>Japanese</th>
<th>Confusable Characters</th>
<th>General Scenario</th>
<th>Pinyin</th>
<th>Vertical Text</th>
<th>Artistic Text</th>
<th>Weighted Average</th>
</tr>
</thead>
<tbody>
<tr>
<td>PP-OCRv5_server_rec</td>
<td><b>0.5807</b></td>
<td><b>0.5806</b></td>
<td><b>0.9013</b></td>
<td><b>0.8679</b></td>
<td><b>0.7472</b></td>
<td><b>0.6039</b></td>
<td><b>0.7372</b></td>
<td><b>0.5946</b></td>
<td><b>0.8384</b></td>
<td><b>0.7435</b></td>
<td><b>0.9314</b></td>
<td><b>0.6397</b></td>
<td><b>0.8401</b></td>
</tr>
<tr>
<td>PP-OCRv4_server_rec</td>
<td>0.3626</td>
<td>0.2661</td>
<td>0.8486</td>
<td>0.6677</td>
<td>0.4097</td>
<td>0.3080</td>
<td>0.4623</td>
<td>0.5028</td>
<td>0.8362</td>
<td>0.2694</td>
<td>0.5455</td>
<td>0.5892</td>
<td>0.5735</td>
</tr>
<tr>
<td>PP-OCRv5_mobile_rec</td>
<td><b>0.4166</b></td>
<td><b>0.4944</b></td>
<td><b>0.8605</b></td>
<td><b>0.8753</b></td>
<td><b>0.7199</b></td>
<td><b>0.5786</b></td>
<td><b>0.7577</b></td>
<td><b>0.5570</b></td>
<td>0.7703</td>
<td><b>0.7248</b></td>
<td><b>0.8089</b></td>
<td>0.5398</td>
<td><b>0.8015</b></td>
</tr>
<tr>
<td>PP-OCRv4_mobile_rec</td>
<td>0.2980</td>
<td>0.2550</td>
<td>0.8398</td>
<td>0.6598</td>
<td>0.3218</td>
<td>0.2593</td>
<td>0.4724</td>
<td>0.4599</td>
<td><b>0.8106</b></td>
<td>0.2593</td>
<td>0.5924</td>
<td><b>0.5555</b></td>
<td>0.5301</td>
</tr>
</tbody>
</table>
A single model can cover multiple languages and text types, with recognition accuracy significantly ahead of previous generation products and mainstream open-source solutions.
# PP-OCRv5 Demo Examples
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-OCRv5/algorithm_ppocrv5_demo1.png" width="600"/>
</div>
<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/PP-OCRv5/algorithm_ppocrv5_demo.pdf">More Demos</a>
## Reference Data for Inference Performance
Test Environment:
- NVIDIA Tesla V100
- Intel Xeon Gold 6271C
- PaddlePaddle 3.0.0
Tested on 200 images (including both general and document images). During testing, images are read from disk, so the image reading time and other associated overhead are also included in the total time consumption. If the images are preloaded into memory, the average time per image can be further reduced by approximately 25 ms.
Unless otherwise specified:
- PP-OCRv4_mobile_det and PP-OCRv4_mobile_rec models are used.
- Document orientation classification, image correction, and text line orientation classification are not used.
- `text_det_limit_type` is set to `"min"` and `text_det_limit_side_len` to `732`.
### 1. Comparison of Inference Performance Between PP-OCRv5 and PP-OCRv4
| Config | Description |
| --------------- | ------------------------------------------------------------ |
| v5_mobile | Uses PP-OCRv5_mobile_det and PP-OCRv5_mobile_rec models. |
| v4_mobile | Uses PP-OCRv4_mobile_det and PP-OCRv4_mobile_rec models. |
| v5_server | Uses PP-OCRv5_server_det and PP-OCRv5_server_rec models. |
| v4_server | Uses PP-OCRv4_server_det and PP-OCRv4_server_rec models. |
**GPU, without high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) | Avg. GPU Utilization (%) | Peak VRAM Usage (MB) | Avg. VRAM Usage (MB) |
| ------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- | ------------------------ | -------------------- | -------------------- |
| v5_mobile | 0.56 | 1162 | 106.02 | 1576.43 | 1420.83 | 18.95 | 4342.00 | 3258.95 |
| v4_mobile | 0.27 | 2246 | 111.20 | 1392.22 | 1318.76 | 28.90 | 1304.00 | 1166.46 |
| v5_server | 0.70 | 929 | 105.31 | 1634.85 | 1428.55 | 36.21 | 5402.00 | 4685.13 |
| v4_server | 0.44 | 1418 | 106.96 | 1455.34 | 1346.95 | 58.82 | 6760.00 | 5817.46 |
**GPU, with high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) | Avg. GPU Utilization (%) | Peak VRAM Usage (MB) | Avg. VRAM Usage (MB) |
| ------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- | ------------------------ | -------------------- | -------------------- |
| v5_mobile | 0.50 | 1301 | 106.50 | 1338.12 | 1155.86 | 11.97 | 4112.00 | 3536.36 |
| v4_mobile | 0.21 | 2887 | 114.09 | 1113.27 | 1054.46 | 15.22 | 2072.00 | 1840.59 |
| v5_server | 0.60 | 1084 | 105.73 | 1980.73 | 1776.20 | 22.10 | 12150.00 | 11849.40 |
| v4_server | 0.36 | 1687 | 104.15 | 1186.42 | 1065.67 | 38.12 | 13058.00 | 12679.00 |
**CPU, without high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) |
| ------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- |
| v5_mobile | 1.43 | 455 | 798.93 | 11695.40 | 6829.09 |
| v4_mobile | 1.09 | 556 | 813.16 | 11996.30 | 6834.25 |
| v5_server | 3.79 | 172 | 799.24 | 50216.00 | 27902.40 |
| v4_server | 4.22 | 148 | 803.74 | 51428.70 | 28593.60 |
**CPU, with high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) |
| ------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- |
| v5_mobile | 1.14 | 571 | 339.68 | 3245.17 | 2560.55 |
| v4_mobile | 0.68 | 892 | 443.00 | 3057.38 | 2329.44 |
| v5_server | 3.56 | 183 | 797.03 | 45664.70 | 26905.90 |
| v4_server | 4.22 | 148 | 803.74 | 51428.70 | 28593.60 |
> Note: PP-OCRv5 uses a larger dictionary in the recognition model, which increases inference time and causes slower performance compared to PP-OCRv4.
### 2. Impact of Auxiliary Features on PP-OCRv5 Inference Performance
| Config | Description |
| --------------- | --------------------------------------------------------------------------------------------------------- |
| base | No document orientation classification, no image correction, no text line orientation classification. |
| with_textline | Includes text line orientation classification only. |
| with_all | Includes document orientation classification, image correction, and text line orientation classification. |
**GPU, without high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) | Avg. GPU Utilization (%) | Peak VRAM Usage (MB) | Avg. VRAM Usage (MB) |
| -------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- | ------------------------ | -------------------- | -------------------- |
| base | 0.56 | 1162 | 106.02 | 1576.43 | 1420.83 | 18.95 | 4342.00 | 3258.95 |
| with_textline | 0.59 | 1104 | 105.58 | 1765.64 | 1478.53 | 19.48 | 4350.00 | 3267.77 |
| with_all | 1.02 | 600 | 104.92 | 1924.23 | 1628.50 | 10.96 | 2632.00 | 2217.01 |
**CPU, without high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) |
| -------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- |
| base | 1.43 | 455 | 798.93 | 11695.40 | 6829.09 |
| with_textline | 1.50 | 434 | 799.47 | 12007.20 | 6882.22 |
| with_all | 1.93 | 316 | 646.49 | 11759.60 | 6940.54 |
> Note: Auxiliary features such as image unwarping can impact inference accuracy. More features do not necessarily yield better results and may increase resource usage.
### 3. Impact of Input Scaling Strategy in Text Detection Module on PP-OCRv5 Inference Performance
| Config | Description |
| ----------------- | -------------------------------------------------------------------------------------- |
| mobile_min_1280 | Uses `min` limit type and `text_det_limit_side_len=1280` with PP-OCRv5_mobile models. |
| mobile_min_736 | Same as default, `min`, `side_len=736`. |
| mobile_max_960 | Uses `max` limit type and `side_len=960`. |
| mobile_max_640 | Uses `max` limit type and `side_len=640`. |
| server_min_1280 | Uses `min`, `side_len=1280` with PP-OCRv5_server models. |
| server_min_736 | Same as default, `min`, `side_len=736`. |
| server_max_960 | Uses `max`, `side_len=960`. |
| server_max_640 | Uses `max`, `side_len=640`. |
**GPU, without high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) | Avg. GPU Utilization (%) | Peak VRAM Usage (MB) | Avg. VRAM Usage (MB) |
| ----------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- | ------------------------ | -------------------- | -------------------- |
| mobile_min_1280 | 0.61 | 1071 | 109.12 | 1663.71 | 1439.72 | 19.27 | 4202.00 | 3550.32 |
| mobile_min_736 | 0.56 | 1162 | 106.02 | 1576.43 | 1420.83 | 18.95 | 4342.00 | 3258.95 |
| mobile_max_960 | 0.48 | 1313 | 103.49 | 1587.25 | 1395.48 | 19.37 | 2642.00 | 2319.03 |
| mobile_max_640 | 0.42 | 1436 | 103.07 | 1651.14 | 1422.62 | 18.95 | 2530.00 | 2149.11 |
| server_min_1280 | 0.82 | 795 | 107.17 | 1678.16 | 1428.94 | 40.43 | 10368.00 | 8320.43 |
| server_min_736 | 0.70 | 929 | 105.31 | 1634.85 | 1428.55 | 36.21 | 5402.00 | 4685.13 |
| server_max_960 | 0.59 | 1073 | 103.03 | 1590.19 | 1383.62 | 33.42 | 2928.00 | 2079.47 |
| server_max_640 | 0.54 | 1099 | 102.63 | 1602.09 | 1416.49 | 30.77 | 3152.00 | 2737.81 |
**CPU, without high-performance inference:**
| Configuration | Avg. Time per Image (s) | Avg. Characters Predicted per Second | Avg. CPU Utilization (%) | Peak RAM Usage (MB) | Avg. RAM Usage (MB) |
| ----------------- | ----------------------- | ------------------------------------ | ------------------------ | ------------------- | ------------------- |
| mobile_min_1280 | 1.64 | 398 | 799.45 | 12344.10 | 7100.60 |
| mobile_min_736 | 1.43 | 455 | 798.93 | 11695.40 | 6829.09 |
| mobile_max_960 | 1.21 | 521 | 800.13 | 11099.10 | 6369.49 |
| mobile_max_640 | 1.01 | 597 | 802.52 | 9585.48 | 5573.52 |
| server_min_1280 | 4.48 | 145 | 800.49 | 50683.10 | 28273.30 |
| server_min_736 | 3.79 | 172 | 799.24 | 50216.00 | 27902.40 |
| server_max_960 | 2.67 | 237 | 797.63 | 49362.50 | 26075.60 |
| server_max_640 | 2.36 | 251 | 795.18 | 45656.10 | 24900.80 |
# Deployment and Secondary Development
* **Multiple System Support**: Compatible with mainstream operating systems including Windows, Linux, and Mac.
* **Multiple Hardware Support**: Besides NVIDIA GPUs, it also supports inference and deployment on Intel CPU, Kunlun chips, Ascend, and other new hardware.
* **High-Performance Inference Plugin**: Recommended to combine with high-performance inference plugins to further improve inference speed. See [High-Performance Inference Guide](../../deployment/high_performance_inference.md) for details.
* **Service Deployment**: Supports highly stable service deployment solutions. See [Service Deployment Guide](../../deployment/serving.md) for details.
* **Secondary Development Capability**: Supports custom dataset training, dictionary extension, and model fine-tuning. Example: To add Korean recognition, you can extend the dictionary and fine-tune the model, seamlessly integrating into existing pipelines. See [Text Detection Module Usage Tutorial](../../module_usage/text_detection.en.md) and [Text Recognition Module Usage Tutorial](../../module_usage/text_recognition.en.md) for details.

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# 一、PP-OCRv5简介
**PP-OCRv5** 是PP-OCR新一代文字识别解决方案，该方案聚焦于多场景、多文字类型的文字识别。在文字类型方面，PP-OCRv5支持简体中文、中文拼音、繁体中文、英文、日文5大主流文字类型，在场景方面，PP-OCRv5升级了中英复杂手写体、竖排文本、生僻字等多种挑战性场景的识别能力。在内部多场景复杂评估集上，PP-OCRv5较PP-OCRv4端到端提升13个百分点。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-OCRv5/algorithm_ppocrv5.png" width="600"/>
</div>
# 二、关键指标
### 1. 文本检测指标
<table>
<thead>
<tr>
<th>模型</th>
<th>手写中文</th>
<th>手写英文</th>
<th>印刷中文</th>
<th>印刷英文</th>
<th>繁体中文</th>
<th>古籍文本</th>
<th>日文</th>
<th>通用场景</th>
<th>拼音</th>
<th>旋转</th>
<th>扭曲</th>
<th>艺术字</th>
<th>平均</th>
</tr>
</thead>
<tbody>
<tr>
<td><b>PP-OCRv5_server_det</b></td>
<td><b>0.803</b></td>
<td><b>0.841</b></td>
<td><b>0.945</b></td>
<td><b>0.917</b></td>
<td><b>0.815</b></td>
<td><b>0.676</b></td>
<td><b>0.772</b></td>
<td><b>0.797</b></td>
<td><b>0.671</b></td>
<td><b>0.8</b></td>
<td><b>0.876</b></td>
<td><b>0.673</b></td>
<td><b>0.827</b></td>
</tr>
<tr>
<td>PP-OCRv4_server_det</td>
<td>0.706</td>
<td>0.249</td>
<td>0.888</td>
<td>0.690</td>
<td>0.759</td>
<td>0.473</td>
<td>0.685</td>
<td>0.715</td>
<td>0.542</td>
<td>0.366</td>
<td>0.775</td>
<td>0.583</td>
<td>0.662</td>
</tr>
<tr>
<td><b>PP-OCRv5_mobile_det</b></td>
<td><b>0.744</b></td>
<td><b>0.777</b></td>
<td><b>0.905</b></td>
<td><b>0.910</b></td>
<td><b>0.823</b></td>
<td><b>0.581</b></td>
<td><b>0.727</b></td>
<td><b>0.721</b></td>
<td><b>0.575</b></td>
<td><b>0.647</b></td>
<td><b>0.827</b></td>
<td>0.525</td>
<td><b>0.770</b></td>
</tr>
<tr>
<td>PP-OCRv4_mobile_det</td>
<td>0.583</td>
<td>0.369</td>
<td>0.872</td>
<td>0.773</td>
<td>0.663</td>
<td>0.231</td>
<td>0.634</td>
<td>0.710</td>
<td>0.430</td>
<td>0.299</td>
<td>0.715</td>
<td><b>0.549</b></td>
<td>0.624</td>
</tr>
</tbody>
</table>
对比PP-OCRv4，PP-OCRv5在所有检测场景下均有明显提升，尤其在手写、古籍、日文检测能力上表现更优。
### 2. 文本识别指标
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-OCRv5/ocrv5_rec_acc.png" width="600"/>
</div>
<table>
<thead>
<tr>
<th>评估集类别</th>
<th>手写中文</th>
<th>手写英文</th>
<th>印刷中文</th>
<th>印刷英文</th>
<th>繁体中文</th>
<th>古籍文本</th>
<th>日文</th>
<th>易混淆字符</th>
<th>通用场景</th>
<th>拼音</th>
<th>竖直文本</th>
<th>艺术字</th>
<th>加权平均</th>
</tr>
</thead>
<tbody>
<tr>
<td>PP-OCRv5_server_rec</td>
<td><b>0.5807</b></td>
<td><b>0.5806</b></td>
<td><b>0.9013</b></td>
<td><b>0.8679</b></td>
<td><b>0.7472</b></td>
<td><b>0.6039</b></td>
<td><b>0.7372</b></td>
<td><b>0.5946</b></td>
<td><b>0.8384</b></td>
<td><b>0.7435</b></td>
<td><b>0.9314</b></td>
<td><b>0.6397</b></td>
<td><b>0.8401</b></td>
</tr>
<tr>
<td>PP-OCRv4_server_rec</td>
<td>0.3626</td>
<td>0.2661</td>
<td>0.8486</td>
<td>0.6677</td>
<td>0.4097</td>
<td>0.3080</td>
<td>0.4623</td>
<td>0.5028</td>
<td>0.8362</td>
<td>0.2694</td>
<td>0.5455</td>
<td>0.5892</td>
<td>0.5735</td>
</tr>
<tr>
<td>PP-OCRv5_mobile_rec</td>
<td><b>0.4166</b></td>
<td><b>0.4944</b></td>
<td><b>0.8605</b></td>
<td><b>0.8753</b></td>
<td><b>0.7199</b></td>
<td><b>0.5786</b></td>
<td><b>0.7577</b></td>
<td><b>0.5570</b></td>
<td>0.7703</td>
<td><b>0.7248</b></td>
<td><b>0.8089</b></td>
<td>0.5398</td>
<td><b>0.8015</b></td>
</tr>
<tr>
<td>PP-OCRv4_mobile_rec</td>
<td>0.2980</td>
<td>0.2550</td>
<td>0.8398</td>
<td>0.6598</td>
<td>0.3218</td>
<td>0.2593</td>
<td>0.4724</td>
<td>0.4599</td>
<td><b>0.8106</b></td>
<td>0.2593</td>
<td>0.5924</td>
<td><b>0.5555</b></td>
<td>0.5301</td>
</tr>
</tbody>
</table>
单模型即可覆盖多语言和多类型文本，识别精度大幅领先前代产品和主流开源方案。
# 三、PP-OCRv5 Demo示例
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-OCRv5/algorithm_ppocrv5_demo1.png" width="600"/>
</div>
<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/PP-OCRv5/algorithm_ppocrv5_demo.pdf">更多示例</a>
## 四、推理性能参考数据
测试环境：
- NVIDIA Tesla V100
- Intel Xeon Gold 6271C
- PaddlePaddle 3.0.0
在 200 张图像（包括通用图像与文档图像）上测试。测试时从磁盘读取图像，因此读图时间及其他额外开销也被包含在总耗时内。如果将图像提前载入到内存，可进一步减少平均每图约 25 ms 的时间开销。
如果不特别说明，则：
- 使用 PP-OCRv4_mobile_det 和 PP-OCRv4_mobile_rec 模型。
- 不使用文档图像方向分类、文本图像矫正、文本行方向分类。
- 将 `text_det_limit_type` 设置为 `"min"`、`text_det_limit_side_len` 设置为 `732`。
### 1. PP-OCRv5 与 PP-OCRv4 推理性能对比
| 配置 | 说明 |
| --- | --- |
| v5_mobile | 使用 PP-OCRv5_mobile_det 和 PP-OCRv5_mobile_rec 模型。 |
| v4_mobile | 使用 PP-OCRv4_mobile_det 和 PP-OCRv4_mobile_rec 模型。 |
| v5_server | 使用 PP-OCRv5_server_det 和 PP-OCRv5_server_rec 模型。 |
| v4_server | 使用 PP-OCRv4_server_det 和 PP-OCRv4_server_rec 模型。 |
**GPU，不使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） | 平均GPU利用率（%） | 峰值 VRAM 用量（MB） | 平均 VRAM 用量（MB） |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| v5_mobile | 0.56 | 1162 | 106.02 | 1576.43 | 1420.83 | 18.95 | 4342.00 | 3258.95 |
| v4_mobile | 0.27 | 2246 | 111.20 | 1392.22 | 1318.76 | 28.90 | 1304.00 | 1166.46 |
| v5_server | 0.70 | 929 | 105.31 | 1634.85 | 1428.55 | 36.21 | 5402.00 | 4685.13 |
| v4_server | 0.44 | 1418 | 106.96 | 1455.34 | 1346.95 | 58.82 | 6760.00 | 5817.46 |
**GPU，使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） | 平均GPU利用率（%） | 峰值 VRAM 用量（MB） | 平均 VRAM 用量（MB） |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| v5_mobile | 0.50 | 1301 | 106.50 | 1338.12 | 1155.86 | 11.97 | 4112.00 | 3536.36 |
| v4_mobile | 0.21 | 2887 | 114.09 | 1113.27 | 1054.46 | 15.22 | 2072.00 | 1840.59 |
| v5_server | 0.60 | 1084 | 105.73 | 1980.73 | 1776.20 | 22.10 | 12150.00 | 11849.40 |
| v4_server | 0.36 | 1687 | 104.15 | 1186.42 | 1065.67 | 38.12 | 13058.00 | 12679.00 |
**CPU，不使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） |
| --- | --- | --- | --- | --- | --- |
| v5_mobile | 1.43 | 455 | 798.93 | 11695.40 | 6829.09 |
| v4_mobile | 1.09 | 556 | 813.16 | 11996.30 | 6834.25 |
| v5_server | 3.79 | 172 | 799.24 | 50216.00 | 27902.40 |
| v4_server | 4.22 | 148 | 803.74 | 51428.70 | 28593.60 |
**CPU，使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） |
| --- | --- | --- | --- | --- | --- |
| v5_mobile | 1.14 | 571 | 339.68 | 3245.17 | 2560.55 |
| v4_mobile | 0.68 | 892 | 443.00 | 3057.38 | 2329.44 |
| v5_server | 3.56 | 183 | 797.03 | 45664.70 | 26905.90 |
| v4_server | 4.22 | 148 | 803.74 | 51428.70 | 28593.60 |
> 说明：PP-OCRv5 的识别模型使用了更大的字典，需要更长的推理时间，导致 PP-OCRv5 的推理速度慢于 PP-OCRv4。
### 2. 使用辅助功能对 PP-OCRv5 推理性能的影响
| 配置 | 说明 |
| --- | --- |
| base | 不使用文档图像方向分类、文本图像矫正、文本行方向分类。 |
| with_textline | 使用文本行方向分类，不使用文档图像方向分类、文本图像矫正。 |
| with_all | 使用文档图像方向分类、文本图像矫正、文本行方向分类。 |
**GPU，不使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） | 平均GPU利用率（%） | 峰值 VRAM 用量（MB） | 平均 VRAM 用量（MB） |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| base | 0.56 | 1162 | 106.02 | 1576.43 | 1420.83 | 18.95 | 4342.00 | 3258.95 |
| with_textline | 0.59 | 1104 | 105.58 | 1765.64 | 1478.53 | 19.48 | 4350.00 | 3267.77 |
| with_all | 1.02 | 600 | 104.92 | 1924.23 | 1628.50 | 10.96 | 2632.00 | 2217.01 |
**CPU，不使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） |
| --- | --- | --- | --- | --- | --- |
| base | 1.43 | 455 | 798.93 | 11695.40 | 6829.09 |
| with_textline | 1.50 | 434 | 799.47 | 12007.20 | 6882.22 |
| with_all | 1.93 | 316 | 646.49 | 11759.60 | 6940.54 |
> 说明：文本图像矫正等辅助功能会对端到端推理精度造成影响，因此并不一定使用的辅助功能越多、资源用量越大。
### 3. 文本检测模块输入缩放尺寸策略对 PP-OCRv5 推理性能的影响
| 配置 | 说明 |
| --- | --- |
| mobile_min_1280 | 使用 PP-OCRv5_mobile_det 和 PP-OCRv5_mobile_rec 模型，将 `text_det_limit_type` 设置为 `"min"`、`text_det_limit_side_len` 设置为 `1280`。 |
| mobile_min_736 | 使用 PP-OCRv5_mobile_det 和 PP-OCRv5_mobile_rec 模型，将 `text_det_limit_type` 设置为 `"min"`、`text_det_limit_side_len` 设置为 `1280`。 |
| mobile_max_960 | 使用 PP-OCRv5_mobile_det 和 PP-OCRv5_mobile_rec 模型，将 `text_det_limit_type` 设置为 `"max"`、`text_det_limit_side_len` 设置为 `960`。 |
| mobile_max_640 | 使用 PP-OCRv5_mobile_det 和 PP-OCRv5_mobile_rec 模型，将 `text_det_limit_type` 设置为 `"max"`、`text_det_limit_side_len` 设置为 `640`。 |
| server_min_1280 | 使用 PP-OCRv5_server_det 和 PP-OCRv5_server_rec 模型，将 `text_det_limit_type` 设置为 `"min"`、`text_det_limit_side_len` 设置为 `1280`。 |
| server_min_736 | 使用 PP-OCRv5_server_det 和 PP-OCRv5_server_rec 模型，将 `text_det_limit_type` 设置为 `"min"`、`text_det_limit_side_len` 设置为 `1280`。 |
| server_max_960 | 使用 PP-OCRv5_server_det 和 PP-OCRv5_server_rec 模型，将 `text_det_limit_type` 设置为 `"max"`、`text_det_limit_side_len` 设置为 `960`。 |
| server_max_640 | 使用 PP-OCRv5_server_det 和 PP-OCRv5_server_rec 模型，将 `text_det_limit_type` 设置为 `"max"`、`text_det_limit_side_len` 设置为 `640`。 |
**GPU，不使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） | 平均GPU利用率（%） | 峰值 VRAM 用量（MB） | 平均 VRAM 用量（MB） |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| mobile_min_1280 | 0.61 | 1071 | 109.12 | 1663.71 | 1439.72 | 19.27 | 4202.00 | 3550.32 |
| mobile_min_736 | 0.56 | 1162 | 106.02 | 1576.43 | 1420.83 | 18.95 | 4342.00 | 3258.95 |
| mobile_max_960 | 0.48 | 1313 | 103.49 | 1587.25 | 1395.48 | 19.37 | 2642.00 | 2319.03 |
| mobile_max_640 | 0.42 | 1436 | 103.07 | 1651.14 | 1422.62 | 18.95 | 2530.00 | 2149.11 |
| server_min_1280 | 0.82 | 795 | 107.17 | 1678.16 | 1428.94 | 40.43 | 10368.00 | 8320.43 |
| server_min_736 | 0.70 | 929 | 105.31 | 1634.85 | 1428.55 | 36.21 | 5402.00 | 4685.13 |
| server_max_960 | 0.59 | 1073 | 103.03 | 1590.19 | 1383.62 | 33.42 | 2928.00 | 2079.47 |
| server_max_640 | 0.54 | 1099 | 102.63 | 1602.09 | 1416.49 | 30.77 | 3152.00 | 2737.81 |
**CPU，不使用高性能推理：**
| 配置 | 平均每图耗时（s） | 平均每秒预测字符数量 | 平均 CPU 利用率（%） | 峰值 RAM 用量（MB） | 平均 RAM 用量（MB） |
| --- | --- | --- | --- | --- | --- |
| mobile_min_1280 | 1.64 | 398 | 799.45 | 12344.10 | 7100.60 |
| mobile_min_736 | 1.43 | 455 | 798.93 | 11695.40 | 6829.09 |
| mobile_max_960 | 1.21 | 521 | 800.13 | 11099.10 | 6369.49 |
| mobile_max_640 | 1.01 | 597 | 802.52 | 9585.48 | 5573.52 |
| server_min_1280 | 4.48 | 145 | 800.49 | 50683.10 | 28273.30 |
| server_min_736 | 3.79 | 172 | 799.24 | 50216.00 | 27902.40 |
| server_max_960 | 2.67 | 237 | 797.63 | 49362.50 | 26075.60 |
| server_max_640 | 2.36 | 251 | 795.18 | 45656.10 | 24900.80 |
# 五、部署与二次开发
* **多系统支持**：兼容Windows、Linux、Mac等主流操作系统。
* **多硬件支持**：除了英伟达GPU外，还支持Intel CPU、昆仑芯、昇腾等新硬件推理和部署。
* **高性能推理插件**：推荐结合高性能推理插件进一步提升推理速度，详见[高性能推理指南](../../deployment/high_performance_inference.md)。
* **服务化部署**：支持高稳定性服务化部署方案，详见[服务化部署指南](../../deployment/serving.md)。
* **二次开发能力**：支持自定义数据集训练、字典扩展、模型微调。举例：如需增加韩文识别，可扩展字典并微调模型，无缝集成到现有产线，详见[文本检测模块使用教程](../../module_usage/text_detection.md)及[文本识别模块使用教程](../../module_usage/text_recognition.md)

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---
comments: true
---
# 1. Introduction to PP-OCRv5 Multilingual Text Recognition
PP-OCRv5 is the latest generation of the PP-OCR series text recognition solutions, focusing on text recognition tasks across multiple scenarios and languages. By default, the recognition model supports accurate recognition of five mainstream text types: Simplified Chinese, Chinese Pinyin, Traditional Chinese, English, and Japanese. In addition, PP-OCRv5 provides multilingual recognition capabilities covering 37 languages, including Korean, Spanish, French, Portuguese, German, Italian, Russian, and more (see [Section 4](#4-supported-languages-and-abbreviations) for the full list of supported languages and abbreviations). Compared to the previous PP-OCRv3 version, PP-OCRv5 achieves more than a 30% improvement in recognition accuracy for multilingual tasks.
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/japan_2_res.jpg)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/french_0_res.jpg)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/german_0_res.png)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/korean_1_res.jpg)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/ru_0.jpeg)
## 2. Quick Start
You can use the `--lang` parameter in the command line to specify the text recognition model for your target language when running the general OCR pipeline:
```bash
# Use the `--lang` parameter to specify the French recognition model
paddleocr ocr -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_french01.png \
--lang fr \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
```
For explanations of other command line parameters, please refer to the [command line usage](../../pipeline_usage/OCR.en.md#21-command-line) of the general OCR pipeline. After execution, results will be printed to the terminal:
```bash
{'res': {'input_path': '/root/.paddlex/predict_input/general_ocr_french01.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'dt_polys': array([[[119, 23],
...,
[118, 75]],
...,
[[109, 506],
...,
[108, 556]]], dtype=int16), 'text_det_params': {'limit_side_len': 64, 'limit_type': 'min', 'thresh': 0.3, 'max_side_limit': 4000, 'box_thresh': 0.6, 'unclip_ratio': 1.5}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['mifere; la profpérité & les fuccès ac-', 'compagnent l’homme induftrieux.', 'Quel eft celui qui a acquis des ri-', 'cheffes, qui eft devenu puiffant, qui', 's’eft couvert de gloire, dont l’éloge', 'retentit par-tout, qui fiege au confeil', "du Roi? C'eft celui qui bannit la pa-", "reffe de fa maifon, & qui a dit à l'oifi-", 'veté : tu es mon ennemie.'], 'rec_scores': array([0.98409832, ..., 0.98091048]), 'rec_polys': array([[[119, 23],
...,
[118, 75]],
...,
[[109, 506],
...,
[108, 556]]], dtype=int16), 'rec_boxes': array([[118, ..., 81],
...,
[108, ..., 562]], dtype=int16)}}
```
If you specify `save_path`, the visualization results will be saved in the `save_path` directory. An example visualization is shown below:
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/general_ocr_french01_res.png"/>
You can also use Python code to specify the recognition model for your target language using the `lang` parameter when initializing the general OCR pipeline:
```python
from paddleocr import PaddleOCR
ocr = PaddleOCR(
lang="fr", # Specify the French recognition model via the lang parameter
use_doc_orientation_classify=False, # Disable document orientation classification
use_doc_unwarping=False, # Disable text image unwarping
use_textline_orientation=False, # Disable textline orientation classification
)
result = ocr.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_french01.png")
for res in result:
res.print()
res.save_to_img("output")
res.save_to_json("output")
```
For more details on the `PaddleOCR` class parameters, refer to the [Python script integration](../../pipeline_usage/OCR.en.md#22-python-script-integration) of the general OCR pipeline.
## 3. Benchmark Comparison
| Model | Korean Dataset Accuracy (%) | | Model | Latin Script Languages Dataset Accuracy (%) | | Model | East Slavic Languages Dataset Accuracy (%) |
|--|--|--|--|--|--|--|--|
| korean_PP-OCRv5_mobile_rec | 88.0 | | latin_PP-OCRv5_mobile_rec | 84.7 | | eslav_PP-OCRv5_mobile_rec | 85.8 |
| korean_PP-OCRv3_mobile_rec | 23.0 | | latin_PP-OCRv3_mobile_rec | 37.9 | | cyrillic_PP-OCRv3_mobile_rec| 50.2 |
**Notes:**
- Korean Dataset: PP-OCRv5's latest dataset containing 5,007 Korean text images.
- Latin Script Languages Dataset: The latest PP-OCRv5 recognition dataset, containing 3,111 text images in Latin script languages.
- East Slavic Languages Dataset: PP-OCRv5's latest dataset containing a total of 7,031 Russian, Belarusian, and Ukrainian text images.
## 4. Supported Languages and Abbreviations
| Language | Description | Abbreviation | | Language | Description | Abbreviation |
| --- | --- | --- | ---|--- | --- | --- |
| Chinese | Chinese & English | ch | | Hungarian | Hungarian | hu |
| English | English | en | | Serbian (Latin) | Serbian(latin) | rslatin |
| French | French | fr | | Indonesian | Indonesian | id |
| German | German | de | | Occitan | Occitan | oc |
| Japanese | Japanese | japan | | Icelandic | Icelandic | is |
| Korean | Korean | korean | | Lithuanian | Lithuanian | lt |
| Chinese Traditional | Chinese Traditional | chinese_cht | | Maori | Maori | mi |
| Afrikaans | Afrikaans | af | | Malay | Malay | ms |
| Italian | Italian | it | | Dutch | Dutch | nl |
| Spanish | Spanish | es | | Norwegian | Norwegian | no |
| Bosnian | Bosnian | bs | | Polish | Polish | pl |
| Portuguese | Portuguese | pt | | Slovak | Slovak | sk |
| Czech | Czech | cs | | Slovenian | Slovenian | sl |
| Welsh | Welsh | cy | | Albanian | Albanian | sq |
| Danish | Danish | da | | Swedish | Swedish | sv |
| Estonian | Estonian | et | | Swahili | Swahili | sw |
| Irish | Irish | ga | | Tagalog | Tagalog | tl |
| Croatian | Croatian | hr | | Turkish | Turkish | tr |
| Uzbek | Uzbek | uz | | Latin | Latin | la |
| Russian | Russian | ru | | Belarusian | Belarusian | be |
| Ukrainian | Ukranian | uk | | | | |

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comments: true
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# 一、PP-OCRv5多语种文本识别介绍
PP-OCRv5 是 PP-OCR 系列的最新一代文字识别解决方案，专注于多场景、多语种的文字识别任务。在文字类型支持方面，默认配置的识别模型可准确识别简体中文、中文拼音、繁体中文、英文和日文这五大主流文字类型。同时，PP-OCRv5还提供了覆盖37种语言的多语种识别能力，包括韩文、西班牙文、法文、葡萄牙文、德文、意大利文、俄罗斯文等（具体支持语种及缩写详见[第四节](#四-支持语种及缩写)）。相较于前代 PP-OCRv3 版本，PP-OCRv5 在多语言识别准确率上实现了超过30%的提升。
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/japan_2_res.jpg)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/french_0_res.jpg)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/german_0_res.png)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/korean_1_res.jpg)
![img](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/ru_0.jpeg)
## 二、快速使用
您可以通过在命令行中使用 `--lang` 参数，来使用指定语种的文本识别模型进行通用 OCR 产线的推理：
```bash
# 通过 `--lang` 参数指定使用法语的识别模型
paddleocr ocr -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_french01.png \
--lang fr \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
```
上述命令行的其他参数说明请参考通用 OCR 产线的[命令行使用方式](../../pipeline_usage/OCR.md#21-命令行方式), 运行后结果会被打印到终端上：
```bash
{'res': {'input_path': '/root/.paddlex/predict_input/general_ocr_french01.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'dt_polys': array([[[119, 23],
...,
[118, 75]],
...,
[[109, 506],
...,
[108, 556]]], dtype=int16), 'text_det_params': {'limit_side_len': 64, 'limit_type': 'min', 'thresh': 0.3, 'max_side_limit': 4000, 'box_thresh': 0.6, 'unclip_ratio': 1.5}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['mifere; la profpérité & les fuccès ac-', 'compagnent l’homme induftrieux.', 'Quel eft celui qui a acquis des ri-', 'cheffes, qui eft devenu puiffant, qui', 's’eft couvert de gloire, dont l’éloge', 'retentit par-tout, qui fiege au confeil', "du Roi? C'eft celui qui bannit la pa-", "reffe de fa maifon, & qui a dit à l'oifi-", 'veté : tu es mon ennemie.'], 'rec_scores': array([0.98409832, ..., 0.98091048]), 'rec_polys': array([[[119, 23],
...,
[118, 75]],
...,
[[109, 506],
...,
[108, 556]]], dtype=int16), 'rec_boxes': array([[118, ..., 81],
...,
[108, ..., 562]], dtype=int16)}}
```
若指定了`save_path`，则会保存可视化结果在`save_path`下。可视化结果如下：
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/ocr/general_ocr_french01_res.png"/>
您也可以使用 Python 代码，在通用 OCR 产线初始化时，通过 `lang` 参数来使用指定语种的识别模型：
```python
from paddleocr import PaddleOCR
ocr = PaddleOCR(
lang="fr" # 通过 lang 参数指定使用法语的识别模型
use_doc_orientation_classify=False, # 通过 use_doc_orientation_classify 参数指定不使用文档方向分类模型
use_doc_unwarping=False, # 通过 use_doc_unwarping 参数指定不使用文本图像矫正模型
use_textline_orientation=False, # 通过 use_textline_orientation 参数指定不使用文本行方向分类模型
)
result = ocr.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_french01.png")
for res in result:
res.print()
res.save_to_img("output")
res.save_to_json("output")
```
更过关于 `PaddleOCR` 类参数的说明参考通用 OCR 产线的[脚本方式集成](../../pipeline_usage/OCR.md#22-python脚本方式集成)。
## 三、指标对比
| 模型 |韩语数据集 精度 (%)| | 模型 | 拉丁字母语言数据集 精度 (%)| | 模型| 东斯拉夫语言数据集 精度 (%) |
|--|--|--|--|--|--|--|--|
| korean_PP-OCRv5_mobile_rec | 88.0 | | latin_PP-OCRv5_mobile_rec | 84.7 | | eslav_PP-OCRv5_mobile_rec | 85.8 |
| korean_PP-OCRv3_mobile_rec | 23.0 | | latin_PP-OCRv3_mobile_rec | 37.9 | | cyrillic_PP-OCRv3_mobile_rec| 50.2 |
**注：**
- 韩语数据集：PP-OCRv5 最新构建的包含了 5007 张韩语文本图片的识别数据集。
- 拉丁字母语言数据集：PP-OCRv5 最新构建的包含了 3111 张拉丁字母语言的文本图片识别数据集。
- 东斯拉夫语言数据集：PP-OCRv5 最新构建的包含了俄语、 白俄罗斯语和乌克兰语共计 7031 张文本图片的识别数据集。
## 四、 支持语种及缩写
| 语种 | 描述 | 缩写 | | 语种 | 描述 | 缩写 |
| --- | --- | --- | ---|--- | --- | --- |
| 中文 | Chinese & English | ch | | 匈牙利文 | Hungarian | hu |
| 英文 | English | en | | 塞尔维亚文（latin） | Serbian(latin) | rslatin |
| 法文 | French | fr | | 印度尼西亚文 | Indonesian | id |
| 德文 | German | de | | 欧西坦文 | Occitan | oc |
| 日文 | Japanese | japan | | 冰岛文 | Icelandic | is |
| 韩文 | Korean | korean | | 立陶宛文 | Lithuanian | lt |
| 中文繁体 | Chinese Traditional | chinese_cht | | 毛利文 | Maori | mi |
| 南非荷兰文 | Afrikaans | af | | 马来文 | Malay | ms |
| 意大利文 | Italian | it | | 荷兰文 | Dutch | nl |
| 西班牙文 | Spanish | es | | 挪威文 | Norwegian | no |
| 波斯尼亚文 | Bosnian | bs | | 波兰文 | Polish | pl |
| 葡萄牙文 | Portuguese | pt | | 斯洛伐克文 | Slovak | sk |
| 捷克文 | Czech | cs | | 斯洛文尼亚文 | Slovenian | sl |
| 威尔士文 | Welsh | cy | | 阿尔巴尼亚文 | Albanian | sq |
| 丹麦文 | Danish | da | | 瑞典文 | Swedish | sv |
| 爱沙尼亚文 | Estonian | et | | 西瓦希里文 | Swahili | sw |
| 爱尔兰文 | Irish | ga | | 塔加洛文 | Tagalog | tl |
| 克罗地亚文 | Croatian | hr | | 土耳其文 | Turkish | tr |
| 乌兹别克文 | Uzbek | uz | | 拉丁文 | Latin | la |
| 俄罗斯文 | Russian | ru | | 白俄罗斯文 | Belarusian | be |
| 乌克兰文 | Ukranian | uk | | | | |