updat text_reg module (#2845)

docs/module_usage/tutorials/ocr_modules/text_recognition.en.md

@@ -15,48 +15,75 @@ The text recognition module is the core component of an OCR (Optical Character R
 <th>Recognition Avg Accuracy(%)</th>
 <th>GPU Inference Time (ms)</th>
 <th>CPU Inference Time (ms)</th>
-<th>Model Size (M)</th>
-<th>Description</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
 <tr>
-<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Trained Model</a></td>
+<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-OCRv4_server_rec_doc_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data based on PP-OCRv4_server_rec. It has added the ability to recognize some traditional Chinese characters, Japanese, and special characters, and can support the recognition of more than 15,000 characters. In addition to improving the text recognition capability related to documents, it also enhances the general text recognition capability.</td>
+</tr>
+<tr>
+<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Training Model</a></td>
 <td>78.20</td>
 <td>7.95018</td>
 <td>46.7868</td>
 <td>10.6 M</td>
-<td rowspan="2">PP-OCRv4, developed by Baidu's PaddlePaddle Vision Team, is the next version of the PP-OCRv3 text recognition model. By introducing data augmentation schemes, GTC-NRTR guidance branches, and other strategies, it further improves text recognition accuracy without compromising model inference speed. The model offers both server and mobile versions to meet industrial needs in different scenarios.</td>
+<td>The PP-OCRv4 recognition model is further upgraded based on PP-OCRv3. Under comparable speed conditions, the effect in Chinese and English scenarios is further improved, and the average recognition accuracy of the 80-language multilingual model is increased by more than 8%.</td>
 </tr>
 <tr>
-<td>PP-OCRv4_server_rec </td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">Trained Model</a></td>
+<td>PP-OCRv4_server_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">Training Model</a></td>
 <td>79.20</td>
 <td>7.19439</td>
 <td>140.179</td>
 <td>71.2 M</td>
+<td>A high-precision server-side text recognition model, featuring high accuracy, fast speed, and multilingual support. It is suitable for text recognition tasks in various scenarios.</td>
+</tr>
+<tr>
+<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/en_PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>The ultra-lightweight English text recognition model released by PaddleOCR in May 2023. It is small in size and fast in speed, and can achieve millisecond-level prediction on CPU. Compared with the PP-OCRv3 English model, the recognition accuracy is improved by 6%, and it is suitable for text recognition tasks in various scenarios.</td>
 </tr>
 </table>
 
-<b>Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, handwriting, and more, with 1.1w images for text recognition. GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
+<b>Note: The evaluation set for the above accuracy indicators is the Chinese dataset built by PaddleOCR, covering multiple scenarios such as street view, web images, documents, and handwriting, with 11,000 images included in text recognition. All models' GPU inference time is based on NVIDIA Tesla T4 machine, with precision type of FP32. CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type of FP32.</b>
 
-> ❗ The above list features the <b>2 core models</b> that the image classification module primarily supports. In total, this module supports <b>4 models</b>. The complete list of models is as follows:
+> ❗ The above list features the <b>4 core models</b> that the image classification module primarily supports. In total, this module supports <b>18 models</b>. The complete list of models is as follows:
 
 <details><summary> 👉Model List Details</summary>
 
+* <b>Chinese Recognition Model</b>
+
 <table>
 <tr>
 <th>Model</th><th>Model Download Link</th>
 <th>Recognition Avg Accuracy(%)</th>
 <th>GPU Inference Time (ms)</th>
 <th>CPU Inference Time (ms)</th>
-<th>Model Size (M)</th>
-<th>Description</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
 <tr>
-<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Trained Model</a></td>
+<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-OCRv4_server_rec_doc_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data based on PP-OCRv4_server_rec. It has added the recognition capabilities for some traditional Chinese characters, Japanese, and special characters. The number of recognizable characters is over 15,000. In addition to the improvement in document-related text recognition, it also enhances the general text recognition capability.</td>
+</tr>
+<tr>
+<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Training Model</a></td>
 <td>78.20</td>
 <td>7.95018</td>
 <td>46.7868</td>
 <td>10.6 M</td>
-<td rowspan="2">PP-OCRv4, developed by Baidu's PaddlePaddle Vision Team, is the next version of the PP-OCRv3 text recognition model. By introducing data augmentation schemes, GTC-NRTR guidance branches, and other strategies, it further improves text recognition accuracy without compromising model inference speed. The model offers both server and mobile versions to meet industrial needs in different scenarios.</td>
+<td>The PP-OCRv4 recognition model is an upgrade from PP-OCRv3. Under comparable speed conditions, the effect in Chinese and English scenarios is further improved. The average recognition accuracy of the 80 multilingual models is increased by more than 8%.</td>
 </tr>
 <tr>
 <td>PP-OCRv4_server_rec </td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">Trained Model</a></td>
@@ -64,51 +91,186 @@ The text recognition module is the core component of an OCR (Optical Character R
 <td>7.19439</td>
 <td>140.179</td>
 <td>71.2 M</td>
+<td>A high-precision server text recognition model, featuring high accuracy, fast speed, and multilingual support. It is suitable for text recognition tasks in various scenarios.</td>
+</tr>
+<tr>
+<td>PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>An ultra-lightweight OCR model suitable for mobile applications. It adopts an encoder-decoder structure based on Transformer and enhances recognition accuracy and efficiency through techniques such as data augmentation and mixed precision training. The model size is 10.6M, making it suitable for deployment on resource-constrained devices. It can be used in scenarios such as mobile photo translation and business card recognition.</td>
 </tr>
 </table>
 
-<p><b>Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, handwriting, and more, with 1.1w images for text recognition. GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
+<p><b>Note: The evaluation set for the above accuracy indicators is the Chinese dataset built by PaddleOCR, covering multiple scenarios such as street view, web images, documents, and handwriting. The text recognition includes 11,000 images. The GPU inference time for all models is based on NVIDIA Tesla T4 machines with FP32 precision type. The CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b></p>
+
 <table>
 <tr>
 <th>Model</th><th>Model Download Link</th>
 <th>Recognition Avg Accuracy(%)</th>
 <th>GPU Inference Time (ms)</th>
 <th>CPU Inference Time</th>
-<th>Model Size (M)</th>
-<th>Description</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
 <tr>
-<td>ch_SVTRv2_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ch_SVTRv2_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_SVTRv2_rec_pretrained.pdparams">Trained Model</a></td>
+<td>ch_SVTRv2_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ch_SVTRv2_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_SVTRv2_rec_pretrained.pdparams">Training Model</a></td>
 <td>68.81</td>
 <td>8.36801</td>
 <td>165.706</td>
 <td>73.9 M</td>
-<td rowspan="1">SVTRv2, a server-side text recognition model developed by the OpenOCR team at the Vision and Learning Lab (FVL) of Fudan University, also won first place in the OCR End-to-End Recognition Task of the PaddleOCR Algorithm Model Challenge. Its A-rank end-to-end recognition accuracy is 6% higher than PP-OCRv4.
+<td rowspan="1">
+SVTRv2 is a server text recognition model developed by the OpenOCR team of Fudan University's Visual and Learning Laboratory (FVL). It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the A list is 6% higher than that of PP-OCRv4.
 </td>
 </tr>
 </table>
 
-<p><b>Note: The evaluation set for the above accuracy metrics is the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">OCR End-to-End Recognition Task of the PaddleOCR Algorithm Model Challenge - Track 1</a> A-rank. GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
+<p><b>Note: The evaluation set for the above accuracy indicators is the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge</a> - Task One: OCR End-to-End Recognition Task A list. The GPU inference time for all models is based on NVIDIA Tesla T4 machines with FP32 precision type. The CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b></p>
 <table>
 <tr>
 <th>Model</th><th>Model Download Link</th>
 <th>Recognition Avg Accuracy(%)</th>
 <th>GPU Inference Time (ms)</th>
 <th>CPU Inference Time</th>
-<th>Model Size (M)</th>
-<th>Description</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
 <tr>
-<td>ch_RepSVTR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ch_RepSVTR_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">Trained Model</a></td>
+<td>ch_RepSVTR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ch_RepSVTR_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">Training Model</a></td>
 <td>65.07</td>
 <td>10.5047</td>
 <td>51.5647</td>
 <td>22.1 M</td>
-<td rowspan="1">  RepSVTR, a mobile text recognition model based on SVTRv2, won first place in the OCR End-to-End Recognition Task of the PaddleOCR Algorithm Model Challenge. Its B-rank end-to-end recognition accuracy is 2.5% higher than PP-OCRv4, with comparable inference speed.</td>
+<td rowspan="1">    The RepSVTR text recognition model is a mobile text recognition model based on SVTRv2. It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the B list is 2.5% higher than that of PP-OCRv4, with the same inference speed.</td>
+</tr>
+</table>
+
+<p><b>Note: The evaluation set for the above accuracy indicators is the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge</a> - Task One: OCR End-to-End Recognition Task B list. The GPU inference time for all models is based on NVIDIA Tesla T4 machines with FP32 precision type. The CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b></p>
+
+* <b>English Recognition Model</b>
+
+<table>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>Recognition Avg Accuracy(%)</th>
+<th>GPU Inference Time (ms)</th>
+<th>CPU Inference Time</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+<tr>
+<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/en_PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>[Latest] Further upgraded based on PP-OCRv3, with improved accuracy under comparable speed conditions.</td>
+</tr>
+<tr>
+<td>en_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/en_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Ultra-lightweight model, supporting English and numeric recognition.</td>
+</tr>
+</table>
+
+* <b>Multilingual Recognition Model</b>
+
+<table>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>Recognition Avg Accuracy(%)</th>
+<th>GPU Inference Time (ms)</th>
+<th>CPU Inference Time</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+<tr>
+<td>korean_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/korean_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Korean Recognition</td>
+</tr>
+<tr>
+<td>japan_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/japan_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Japanese Recognition</td>
+</tr>
+<tr>
+<td>chinese_cht_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/chinese_cht_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Traditional Chinese Recognition</td>
+</tr>
+<tr>
+<td>te_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/te_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Telugu Recognition</td>
+</tr>
+<tr>
+<td>ka_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/ka_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Kannada Recognition</td>
+</tr>
+<tr>
+<td>ta_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/ta_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Tamil Recognition</td>
+</tr>
+<tr>
+<td>latin_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/latin_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Latin Recognition</td>
+</tr>
+<tr>
+<td>arabic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/arabic_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Arabic Script Recognition</td>
+</tr>
+<tr>
+<td>cyrillic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/cyrillic_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Cyrillic Script Recognition</td>
+</tr>
+<tr>
+<td>devanagari_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/devanagari_PP-OCRv3_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>Devanagari Script Recognition</td>
 </tr>
 </table>
 
-<p><b>Note: The evaluation set for the above accuracy metrics is the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">OCR End-to-End Recognition Task of the PaddleOCR Algorithm Model Challenge - Track 1</a> B-rank. GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
+</details>
 
 ## III. Quick Integration
 Before quick integration, you need to install the PaddleX wheel package. For the installation method, please refer to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md). After installing the wheel package, a few lines of code can complete the inference of the text recognition module. You can switch models under this module freely, and you can also integrate the model inference of the text recognition module into your project.
@@ -126,6 +288,146 @@ for res in output:
 ```
 For more information on using PaddleX's single-model inference APIs, please refer to the [PaddleX Single-Model Python Script Usage Instructions](../../instructions/model_python_API.en.md).
 
+After running, the result obtained is:
+```bash
+{'input_path': 'general_ocr_rec_001.png', 'rec_text': 'Oasis Shigewei Garden Apartment', 'rec_score': 0.9875298738479614}
+````
+
+The visualized image is as follows:
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/text_recog/general_ocr_rec_001.png">
+
+In the above Python script, the following steps are executed:
+* `create_model` instantiates the text recognition model (here, `PP-OCRv4_mobile_rec` is taken as an example)
+* The `predict` method of the text recognition model is called for inference prediction. The parameter of the `predict` method is `x`, which is used to input the data to be predicted. It supports multiple input types, and the specific instructions are as follows:
+
+<table>
+<thead>
+<tr>
+<th>Parameter</th>
+<th>Parameter Description</th>
+<th>Parameter Type</th>
+<th>Options</th>
+<th>Default Value</th>
+</tr>
+</thead>
+<tr>
+<td><code>x</code></td>
+<td>Data to be predicted, supporting multiple input types</td>
+<td><code>Python Var</code>/<code>str</code>/<code>dict</code>/<code>list</code></td>
+<td>
+<ul>
+  <li><b>Python variable</b>, such as image data represented by <code>numpy.ndarray</code></li>
+  <li><b>File path</b>, such as the local path of an image file: <code>/root/data/img.jpg</code></li>
+  <li><b>URL link</b>, such as the network URL of an image file: <a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_001.png">Example</a></li>
+  <li><b>Local directory</b>, this directory should contain the data files to be predicted, such as the local path: <code>/root/data/</code></li>
+  <li><b>Dictionary</b>, the <code>key</code> of the dictionary should correspond to the specific task, such as <code>"img"</code> for image classification tasks. The <code>value</code> of the dictionary supports the above types of data, for example: <code>{"img": "/root/data1"}</code></li>
+  <li><b>List</b>, the elements of the list should be the above types of data, such as <code>[numpy.ndarray, numpy.ndarray]</code>, <code>["/root/data/img1.jpg", "/root/data/img2.jpg"]</code>, <code>["/root/data1", "/root/data2"]</code>, <code>[{"img": "/root/data1"}, {"img": "/root/data2/img.jpg"}]</code></li>
+</ul>
+</td>
+<td>None</td>
+</tr>
+<tr>
+<td><code>module_name</code></td>
+<td>Name of the single-function module</td>
+<td><code>str</code></td>
+<td>None</td>
+<td><code>text_recognition</code></td>
+</tr>
+<tr>
+<td><code>model_name</code></td>
+<td>Name of the model</td>
+<td><code>str</code></td>
+<td>None</td>
+<td><code>PP-OCRv4_mobile_rec</code></td>
+</tr>
+<tr>
+<td><code>model_dir</code></td>
+<td>Path where the model is stored</td>
+<td><code>str</code></td>
+<td>None</td>
+<td><code>null</code></td>
+</tr>
+<tr>
+<td><code>batch_size</code></td>
+<td>Batch size</td>
+<td><code>int</code></td>
+<td>None</td>
+<td>1</td>
+</tr>
+<tr>
+<td><code>score_thresh</code></td>
+<td>Score threshold</td>
+<td><code>int</code></td>
+<td>None</td>
+<td><code>0</code></td>
+</tr>
+</table>
+
+* Process the prediction results. The prediction result for each sample is of `dict` type, and supports operations such as printing, saving as an image, and saving as a `json` file:
+
+<table>
+<thead>
+<tr>
+<th>Method</th>
+<th>Method Description</th>
+<th>Parameter</th>
+<th>Parameter Type</th>
+<th>Parameter Description</th>
+<th>Default Value</th>
+</tr>
+</thead>
+<tr>
+<td rowspan = "3"><code>print</code></td>
+<td rowspan = "3">Print the result to the terminal</td>
+<td><code>format_json</code></td>
+<td><code>bool</code></td>
+<td>Whether to format the output content using <code>json</code> indentation</td>
+<td><code>True</code></td>
+</tr>
+<tr>
+<td><code>indent</code></td>
+<td><code>int</code></td>
+<td>JSON formatting setting, only effective when <code>format_json</code> is <code>True</code></td>
+<td>4</td>
+</tr>
+<tr>
+<td><code>ensure_ascii</code></td>
+<td><code>bool</code></td>
+<td>JSON formatting setting, only effective when <code>format_json</code> is <code>True</code></td>
+<td><code>False</code></td>
+</tr>
+<tr>
+<td rowspan = "3"><code>save_to_json</code></td>
+<td rowspan = "3">Save the result as a JSON file</td>
+<td><code>save_path</code></td>
+<td><code>str</code></td>
+<td>The path where the file is saved. If it is a directory, the saved file name is consistent with the input file name</td>
+<td>None</td>
+</tr>
+<tr>
+<td><code>indent</code></td>
+<td><code>int</code></td>
+<td>JSON formatting setting</td>
+<td>4</td>
+</tr>
+<tr>
+<td><code>ensure_ascii</code></td>
+<td><code>bool</code></td>
+<td>JSON formatting setting</td>
+<td><code>False</code></td>
+</tr>
+<tr>
+<td><code>save_to_img</code></td>
+<td>Save the result as an image file</td>
+<td><code>save_path</code></td>
+<td><code>str</code></td>
+<td>The path where the file is saved. If it is a directory, the saved file name is consistent with the input file name</td>
+<td>None</td>
+</tr>
+</table>
+
+
 ## IV. Custom Development
 If you are seeking higher accuracy from existing models, you can use PaddleX's custom development capabilities to develop better  text recognition models. Before using PaddleX to develop text recognition models, please ensure that you have installed the relevant model training plugins for OCR in PaddleX. The installation process can be found in the custom development section of the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
 

docs/module_usage/tutorials/ocr_modules/text_recognition.md

@@ -19,12 +19,21 @@ comments: true
 <th>介绍</th>
 </tr>
 <tr>
+<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+PP-OCRv4_server_rec_doc_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>PP-OCRv4_server_rec_doc是在PP-OCRv4_server_rec的基础上，在更多中文文档数据和PP-OCR训练数据的混合数据训练而成，增加了部分繁体字、日文、特殊字符的识别能力，可支持识别的字符为1.5万+，除文档相关的文字识别能力提升外，也同时提升了通用文字的识别能力</td>
+</tr>
+<tr>
 <td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>78.20</td>
 <td>7.95018</td>
 <td>46.7868</td>
 <td>10.6 M</td>
-<td rowspan="2">PP-OCRv4是百度飞桨视觉团队自研的文本识别模型PP-OCRv3的下一个版本，通过引入数据增强方案、GTC-NRTR指导分支等策略，在模型推理速度不变的情况下，进一步提升了文本识别精度。该模型提供了服务端（server）和移动端（mobile）两个不同版本，来满足不同场景下的工业需求。</td>
+<td>PP-OCRv4识别模型在PP-OCRv3的基础上进一步升级，速度可比情况下，中英文场景效果进一步提升，80 语种多语言模型平均识别准确率提升 8%以上</td>
 </tr>
 <tr>
 <td>PP-OCRv4_server_rec </td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">训练模型</a></td>
@@ -32,6 +41,16 @@ comments: true
 <td>7.19439</td>
 <td>140.179</td>
 <td>71.2 M</td>
+<td>高精度服务端文本识别模型，具有高精度、速度快、多语言支持等特点，适用于多种场景的文字识别任务。</td>
+</tr>
+<tr>
+<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+en_PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>PaddleOCR于2023年5月发布的超轻量英文文本识别模型，体积小、速度快，可在CPU上实现毫秒级预测，相比PP-OCRv3英文模型，识别准确率提升6%，适用于多种场景的文字识别任务。</td>
 </tr>
 </table>
 
@@ -41,6 +60,8 @@ comments: true
 
 <details><summary> 👉模型列表详情</summary>
 
+* <b>中文识别模型</b>
+
 <table>
 <tr>
 <th>模型</th><th>模型下载链接</th>
@@ -51,12 +72,21 @@ comments: true
 <th>介绍</th>
 </tr>
 <tr>
+<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+PP-OCRv4_server_rec_doc_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>PP-OCRv4_server_rec_doc是在PP-OCRv4_server_rec的基础上，在更多中文文档数据和PP-OCR训练数据的混合数据训练而成，增加了部分繁体字、日文、特殊字符的识别能力，可支持识别的字符为1.5万+，除文档相关的文字识别能力提升外，也同时提升了通用文字的识别能力</td>
+</tr>
+<tr>
 <td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>78.20</td>
 <td>7.95018</td>
 <td>46.7868</td>
 <td>10.6 M</td>
-<td rowspan="2">PP-OCRv4是百度飞桨视觉团队自研的文本识别模型PP-OCRv3的下一个版本，通过引入数据增强方案、GTC-NRTR指导分支等策略，在模型推理速度不变的情况下，进一步提升了文本识别精度。该模型提供了服务端（server）和移动端（mobile）两个不同版本，来满足不同场景下的工业需求。</td>
+<td>PP-OCRv4识别模型在PP-OCRv3的基础上进一步升级，速度可比情况下，中英文场景效果进一步提升，80 语种多语言模型平均识别准确率提升 8%以上</td>
 </tr>
 <tr>
 <td>PP-OCRv4_server_rec </td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">训练模型</a></td>
@@ -64,10 +94,21 @@ comments: true
 <td>7.19439</td>
 <td>140.179</td>
 <td>71.2 M</td>
+<td>高精度服务端文本识别模型，具有高精度、速度快、多语言支持等特点，适用于多种场景的文字识别任务。</td>
+</tr>
+<tr>
+<td>PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>超轻量级OCR模型，适用于移动端应用。它采用基于Transformer的编码器-解码器结构，通过数据增强和混合精度训练等技术，提升了识别精度和效率。模型大小为10.6M，适合在资源受限的设备上部署，可用于手机拍照翻译、名片识别等场景。</td>
 </tr>
 </table>
 
 <p><b>注：以上精度指标的评估集是 PaddleOCR 自建的中文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 1.1w 张图片。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
+
 <table>
 <tr>
 <th>模型</th><th>模型下载链接</th>
@@ -109,7 +150,143 @@ SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队
 </tr>
 </table>
 
-<p><b>注：以上精度指标的评估集是 <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>B榜。 所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
+<p><b>注：以上精度指标的评估集是 <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>B榜。 所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
+
+* <b>英文识别模型</b>
+
+<table>
+<tr>
+<th>模型</th><th>模型下载链接</th>
+<th>识别 Avg Accuracy(%)</th>
+<th>GPU推理耗时（ms）</th>
+<th>CPU推理耗时</th>
+<th>模型存储大小（M）</th>
+<th>介绍</th>
+</tr>
+<tr>
+<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+en_PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>【最新】在PP-OCRv3的基础上进一步升级，在速度可比的情况下，精度进一步提升</td>
+</tr>
+<tr>
+<td>en_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+en_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>超轻量模型，支持英文、数字识别</td>
+</tr>
+</table>
+
+* <b>多语言识别模型</b>
+
+<table>
+<tr>
+<th>模型</th><th>模型下载链接</th>
+<th>识别 Avg Accuracy(%)</th>
+<th>GPU推理耗时（ms）</th>
+<th>CPU推理耗时</th>
+<th>模型存储大小（M）</th>
+<th>介绍</th>
+</tr>
+<tr>
+<td>korean_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+korean_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>韩文识别</td>
+</tr>
+<tr>
+<td>japan_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+japan_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>日文识别</td>
+</tr>
+<tr>
+<td>chinese_cht_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+chinese_cht_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>中文繁体识别</td>
+</tr>
+<tr>
+<td>te_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+te_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>泰卢固文识别</td>
+</tr>
+<tr>
+<td>ka_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+ka_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>卡纳达文识别</td>
+</tr>
+<tr>
+<td>ta_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+ta_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>泰米尔文识别</td>
+</tr>
+<tr>
+<td>latin_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+latin_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>拉丁文识别</td>
+</tr>
+<tr>
+<td>arabic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+arabic_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>阿拉伯字母</td>
+</tr>
+<tr>
+<td>cyrillic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+cyrillic_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>斯拉夫字母</td>
+</tr>
+<tr>
+<td>devanagari_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/\
+devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>梵文字母</td>
+</tr>
+</table>
+
+</details>
 
 ## 三、快速集成
 在快速集成前，首先需要安装 PaddleX 的 wheel 包，wheel的安装方式请参考[PaddleX本地安装教程](../../../installation/installation.md)。完成 wheel 包的安装后，几行代码即可完成文本识别模块的推理，可以任意切换该模块下的模型，您也可以将文本识别的模块中的模型推理集成到您的项目中。运行以下代码前，请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_rec_001.png)到本地。
@@ -125,6 +302,146 @@ for res in output:
 ```
 关于更多 PaddleX 的单模型推理的 API 的使用方法，可以参考[PaddleX单模型Python脚本使用说明](../../instructions/model_python_API.md)。
 
+运行后，得到的结果为：
+```bash
+{'input_path': 'general_ocr_rec_001.png', 'rec_text': '绿洲仕格维花园公寓', 'rec_score': 0.9875298738479614}
+```
+可视化图片如下：
+
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/text_recog/general_ocr_rec_001.png">
+
+上述Python脚本中，执行了如下几个步骤：
+* `create_model`实例化文本识别模型（此处以`PP-OCRv4_mobile_rec`为例）
+* 调用文本识别模型的`predict`方法进行推理预测，`predict` 方法参数为`x`，用于输入待预测数据，支持多种输入类型，具体说明如下：
+
+<table>
+<thead>
+<tr>
+<th>参数</th>
+<th>参数说明</th>
+<th>参数类型</th>
+<th>可选项</th>
+<th>默认值</th>
+</tr>
+</thead>
+<tr>
+<td><code>x</code></td>
+<td>待预测数据，支持多种输入类型</td>
+<td><code>Python Var</code>/<code>str</code>/<code>dict</code>/<code>list</code></td>
+<td>
+<ul>
+  <li><b>Python变量</b>，如<code>numpy.ndarray</code>表示的图像数据</li>
+  <li><b>文件路径</b>，如图像文件的本地路径：<code>/root/data/img.jpg</code></li>
+  <li><b>URL链接</b>，如图像文件的网络URL：<a href = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_001.png">示例</a></li>
+  <li><b>本地目录</b>，该目录下需包含待预测数据文件，如本地路径：<code>/root/data/</code></li>
+  <li><b>字典</b>，字典的<code>key</code>需与具体任务对应，如图像分类任务对应<code>\"img\"</code>，字典的<code>val</code>支持上述类型数据，例如：<code>{\"img\": \"/root/data1\"}</code></li>
+  <li><b>列表</b>，列表元素需为上述类型数据，如<code>[numpy.ndarray, numpy.ndarray]</code>，<code>[\"/root/data/img1.jpg\", \"/root/data/img2.jpg\"]</code>，<code>[\"/root/data1\", \"/root/data2\"]</code>，<code>[{\"img\": \"/root/data1\"}, {\"img\": \"/root/data2/img.jpg\"}]</code></li>
+</ul>
+</td>
+<td>无</td>
+</tr>
+<tr>
+<td><code>module_name</code></td>
+<td>单功能模块名称</td>
+<td><code>str</code></td>
+<td>无</td>
+<td><code>text_recognition</code></td>
+</tr>
+<tr>
+<td><code>model_name</code></td>
+<td>模型名称</td>
+<td><code>str</code></td>
+<td>无</td>
+<td><code>PP-OCRv4_mobile_rec</code></td>
+</tr>
+<tr>
+<td><code>model_dir</code></td>
+<td>模型存储路径</td>
+<td><code>str</code></td>
+<td>无</td>
+<td><code>null</code></td>
+</tr>
+<tr>
+<td><code>batch_size</code></td>
+<td>批大小</td>
+<td><code>int</code></td>
+<td>无</td>
+<td>1</td>
+</tr>
+<tr>
+<td><code>score_thresh</code></td>
+<td>分数阈值</td>
+<td><code>int</code></td>
+<td>无</td>
+<td><code>0</code></td>
+</tr>
+</table>
+
+* 对预测结果进行处理，每个样本的预测结果均为`dict`类型，且支持打印、保存为图片、保存为`json`文件的操作:
+
+<table>
+<thead>
+<tr>
+<th>方法</th>
+<th>方法说明</th>
+<th>参数</th>
+<th>参数类型</th>
+<th>参数说明</th>
+<th>默认值</th>
+</tr>
+</thead>
+<tr>
+<td rowspan = "3"><code>print</code></td>
+<td rowspan = "3">打印结果到终端</td>
+<td><code>format_json</code></td>
+<td><code>bool</code></td>
+<td>是否对输出内容进行使用<code>json</code>缩进格式化</td>
+<td><code>True</code></td>
+</tr>
+<tr>
+<td><code>indent</code></td>
+<td><code>int</code></td>
+<td>json格式化设置，仅当<code>format_json</code>为<code>True</code>时有效</td>
+<td>4</td>
+</tr>
+<tr>
+<td><code>ensure_ascii</code></td>
+<td><code>bool</code></td>
+<td>json格式化设置，仅当<code>format_json</code>为<code>True</code>时有效</td>
+<td><code>False</code></td>
+</tr>
+<tr>
+<td rowspan = "3"><code>save_to_json</code></td>
+<td rowspan = "3">将结果保存为json格式的文件</td>
+<td><code>save_path</code></td>
+<td><code>str</code></td>
+<td>保存的文件路径，当为目录时，保存文件命名与输入文件类型命名一致</td>
+<td>无</td>
+</tr>
+<tr>
+<td><code>indent</code></td>
+<td><code>int</code></td>
+<td>json格式化设置</td>
+<td>4</td>
+</tr>
+<tr>
+<td><code>ensure_ascii</code></td>
+<td><code>bool</code></td>
+<td>json格式化设置</td>
+<td><code>False</code></td>
+</tr>
+<tr>
+<td><code>save_to_img</code></td>
+<td>将结果保存为图像格式的文件</td>
+<td><code>save_path</code></td>
+<td><code>str</code></td>
+<td>保存的文件路径，当为目录时，保存文件命名与输入文件类型命名一致</td>
+<td>无</td>
+</tr>
+</table>
+
+
+
 ## 四、二次开发
 如果你追求更高精度的现有模型，可以使用 PaddleX 的二次开发能力，开发更好的文本识别模型。在使用 PaddleX 开发文本识别模型之前，请务必安装 PaddleX 的 OCR 相关模型训练插件，安装过程可以参考[PaddleX本地安装教程](../../../installation/installation.md)中的二次开发部分。