OCR.en.md 54 KB
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General OCR Production Line Usage Guide
1. Introduction to the OCR Production Line
OCR (Optical Character Recognition) is a technology that converts text in images into editable text. It is widely used in document digitization, information extraction, and data processing. OCR can recognize printed text, handwritten text, and even certain types of fonts and symbols.
The General OCR production line is designed to solve text recognition tasks, extracting text information from images and outputting it in text form. This production line integrates the well-known end-to-end OCR series systems, PP-OCRv3 and PP-OCRv4, supporting recognition of over 80 languages. Additionally, it includes functions for image orientation correction and distortion correction. Based on this production line, precise text content prediction at the millisecond level on CPUs can be achieved, covering a wide range of applications including general, manufacturing, finance, and transportation sectors. The production line also provides flexible deployment options, supporting calls in various programming languages on multiple hardware platforms. Moreover, it offers the capability for secondary development, allowing you to train and optimize on your own dataset. The trained models can also be seamlessly integrated.
The General OCR production line includes mandatory text detection and text recognition modules, as well as optional document image orientation classification, text image correction, and text line orientation classification modules. The document image orientation classification and text image correction modules are integrated as a document preprocessing sub-line into the General OCR production line. Each module contains multiple models, and you can choose the model based on the benchmark test data below.
If you prioritize model accuracy, choose a high-accuracy model; if you prioritize inference speed, choose a faster inference model; if you care about model storage size, choose a smaller model.
Document Image Orientation Classification Module (Optional):
| Model | Model Download Link | Top-1 Acc (%) | CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| PP-LCNet_x1_0_doc_ori | Inference Model/Training Model | 99.06 | 2.31 / 0.43 | 3.37 / 1.27 | 7 | A document image classification model based on PP-LCNet_x1_0, with four categories: 0 degrees, 90 degrees, 180 degrees, and 270 degrees. |
Text Image Correction Module (Optional):
| Model | Model Download Link | CER | Model Storage Size (M) | Introduction |
|---|---|---|---|---|
| UVDoc | Inference Model/Training Model | 0.179 | 30.3 M | High-precision text image correction model |
Text Detection Module:
| Model | Model Download Link | Detection Hmean (%) | CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| PP-OCRv4_server_det | Inference Model/Training Model | 82.56 | 83.34 / 80.91 | 442.58 / 442.58 | 109 | The server-side text detection model of PP-OCRv4, with higher accuracy, suitable for deployment on high-performance servers |
| PP-OCRv4_mobile_det | Inference Model/Training Model | 77.35 | 8.79 / 3.13 | 51.00 / 28.58 | 4.7 | The mobile text detection model of PP-OCRv4, with higher efficiency, suitable for deployment on edge devices |
| PP-OCRv3_mobile_det | Inference Model/Training Model | 78.68 | 8.44 / 2.91 | 27.87 / 27.87 | 2.1 | The mobile text detection model of PP-OCRv3, with higher efficiency, suitable for deployment on edge devices |
| PP-OCRv3_server_det | Inference Model/Training Model | 80.11 | 65.41 / 13.67 | 305.07 / 305.07 | 102.1 | The server-side text detection model of PP-OCRv3, with higher accuracy, suitable for deployment on high-performance servers |
Text Recognition Module:
| Model | Model Download Link | Recognition Avg Accuracy (%) | CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| PP-OCRv4_server_rec_doc | Inference Model/Training Model | 81.53 | 6.65 / 6.65 | 32.92 / 32.92 | 74.7 M | 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 enhances the recognition of traditional Chinese characters, Japanese, and special characters, supporting over 15,000 characters. It improves both document-related and general text recognition capabilities. |
| PP-OCRv4_mobile_rec | Inference Model/Training Model | 78.74 | 4.82 / 4.82 | 16.74 / 4.64 | 10.6 M | The lightweight recognition model of PP-OCRv4, with high inference efficiency, suitable for deployment on various hardware devices, including edge devices |
| PP-OCRv4_server_rec | Inference Model/Training Model | 80.61 | 6.58 / 6.58 | 33.17 / 33.17 | 71.2 M | The server-side recognition model of PP-OCRv4, with high inference accuracy, suitable for deployment on various servers |
| en_PP-OCRv4_mobile_rec | Inference Model/Training Model | 70.39 | 4.81 / 4.81 | 16.10 / 5.31 | 6.8 M | The ultra-lightweight English recognition model trained based on the PP-OCRv4 recognition model, supporting English and numeric recognition |
❗ The above list features the 4 core models that the text recognition module primarily supports. In total, this module supports 18 models. The complete list of models is as follows:
👉Model List Details
* Chinese Recognition Model| Model | Model Download Link | Recognition Avg Accuracy(%) | CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| PP-OCRv4_server_rec_doc | Inference Model/Training Model | 6.65 / 6.65 | 32.92 / 32.92 | 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. | ||
| PP-OCRv4_mobile_rec | Inference Model/Training Model | 78.20 | 4.82 / 4.82 | 16.74 / 4.64 | 10.6 M | 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%. |
| PP-OCRv4_server_rec | Inference Model/Trained Model | 79.20 | 6.58 / 6.58 | 33.17 / 33.17 | 71.2 M | 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. |
| PP-OCRv3_mobile_rec | Inference Model/Training Model | 5.87 / 5.87 | 9.07 / 4.28 | 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. |
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.
| Model | Model Download Link | Recognition Avg Accuracy(%) | GPU Inference Time (ms) | CPU Inference Time | Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| ch_SVTRv2_rec | Inference Model/Training Model | 68.81 | 8.36801 | 165.706 | 73.9 M | 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. |
Note: The evaluation set for the above accuracy indicators is the PaddleOCR Algorithm Model Challenge - 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.
| Model | Model Download Link | Recognition Avg Accuracy(%) | GPU Inference Time (ms) | CPU Inference Time | Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| ch_RepSVTR_rec | Inference Model/Training Model | 65.07 | 10.5047 | 51.5647 | 22.1 M | 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. |
Note: The evaluation set for the above accuracy indicators is the PaddleOCR Algorithm Model Challenge - 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.
* English Recognition Model| Model | Model Download Link | Recognition Avg Accuracy(%) | GPU Inference Time (ms) | CPU Inference Time | Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| en_PP-OCRv4_mobile_rec | Inference Model/Training Model | [Latest] Further upgraded based on PP-OCRv3, with improved accuracy under comparable speed conditions. | ||||
| en_PP-OCRv3_mobile_rec | Inference Model/Training Model | Ultra-lightweight model, supporting English and numeric recognition. |
| Model | Model Download Link | Recognition Avg Accuracy(%) | GPU Inference Time (ms) | CPU Inference Time | Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| korean_PP-OCRv3_mobile_rec | Inference Model/Training Model | Korean Recognition | ||||
| japan_PP-OCRv3_mobile_rec | Inference Model/Training Model | Japanese Recognition | ||||
| chinese_cht_PP-OCRv3_mobile_rec | Inference Model/Training Model | Traditional Chinese Recognition | ||||
| te_PP-OCRv3_mobile_rec | Inference Model/Training Model | Telugu Recognition | ||||
| ka_PP-OCRv3_mobile_rec | Inference Model/Training Model | Kannada Recognition | ||||
| ta_PP-OCRv3_mobile_rec | Inference Model/Training Model | Tamil Recognition | ||||
| latin_PP-OCRv3_mobile_rec | Inference Model/Training Model | Latin Recognition | ||||
| arabic_PP-OCRv3_mobile_rec | Inference Model/Training Model | Arabic Script Recognition | ||||
| cyrillic_PP-OCRv3_mobile_rec | Inference Model/Training Model | Cyrillic Script Recognition | ||||
| devanagari_PP-OCRv3_mobile_rec | Inference Model/Training Model | Devanagari Script Recognition |
Text Line Orientation Classification Module (Optional):
| Model | Model Download Link | Top-1 Acc (%) | CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (M) | Introduction |
|---|---|---|---|---|---|---|
| PP-LCNet_x0_25_textline_ori | Inference Model/ Training Model | 95.54 | - | - | 0.32 | A text line orientation classification model based on PP-LCNet_x0_25, with two categories: 0 degrees and 180 degrees. |
If you are satisfied with the performance of the production line, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can fine-tune the models in the production line using your private data. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
### 2.2 Local Experience
> ❗ Before using the general OCR production line locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
#### 2.2.1 Command Line Experience
* You can quickly experience the OCR production line with a single command. Use the [test image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png), and replace `--input` with the local path for prediction.
```bash
paddlex --pipeline OCR \
--input general_ocr_002.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
```
For details on the relevant parameter descriptions, please refer to the parameter descriptions in [2.2.2 Python Script Integration](#222-python-script-integration).
After running, the results will be printed to the terminal as follows:
```bash
{'res': {'input_path': 'general_ocr_002.png', 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': '0.jpg', 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': False}, 'angle': 0},'dt_polys': [array([[ 3, 10],
[82, 10],
[82, 33],
[ 3, 33]], dtype=int16), ...], 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': [-1, ...], 'text_rec_score_thresh': 0.0, 'rec_texts': ['www.99*', ...], 'rec_scores': [0.8980069160461426, ...], 'rec_polys': [array([[ 3, 10],
[82, 10],
[82, 33],
[ 3, 33]], dtype=int16), ...], 'rec_boxes': array([[ 3, 10, 82, 33], ...], dtype=int16)}}
```
The visualized results are saved under `save_path`, and the OCR visualization results are as follows:
#### 2.2.2 Python Script Integration
* The above command line is for quick experience and effect checking. Generally, in a project, integration through code is often required. You can complete the quick inference of the production line with just a few lines of code. The inference code is as follows:
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="OCR")
output = pipeline.predict(
input="./general_ocr_002.png",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
use_textline_orientation=False,
)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
```
In the above Python script, the following steps are executed:
(1) The OCR production line object is instantiated via `create_pipeline()`, with specific parameter descriptions as follows:
| Parameter | Description | Type | Default Value |
|---|---|---|---|
pipeline |
The name of the production line or the path to the production line configuration file. If it is a production line name, it must be supported by PaddleX. | str |
None |
config |
Specific configuration information for the pipeline (if set simultaneously with the pipeline, it takes precedence over the pipeline, and the pipeline name must match the pipeline).
|
dict[str, Any] |
None |
device |
The device used for production line inference. It supports specifying specific GPU card numbers, such as "gpu:0", other hardware card numbers, such as "npu:0", or CPU, such as "cpu". | str |
gpu:0 |
use_hpip |
Whether to enable high-performance inference. This is only available if the production line supports high-performance inference. | bool |
False |
| Parameter | Description | Type | Options | Default Value |
|---|---|---|---|---|
input |
Data to be predicted, supports multiple input types (required). | Python Var|str|list |
|
None |
device |
The device used for inference. | str|None |
|
None |
use_doc_orientation_classify |
Whether to use the document orientation classification module. | bool|None |
|
None |
use_doc_unwarping |
Whether to use the document unwarping module. | bool|None |
|
None |
use_textline_orientation |
Whether to use the text line orientation classification module. | bool|None |
|
None |
text_det_limit_side_len |
The limit on the side length of the image for text detection. | int|None |
|
None |
text_det_limit_type |
The type of limit on the side length of the image for text detection. | str|None |
|
None |
text_det_thresh |
The detection pixel threshold. Pixels with scores greater than this threshold in the output probability map will be considered as text pixels. | float|None |
|
None |
text_det_box_thresh |
The detection box threshold. A detection result will be considered as a text region if the average score of all pixels within the bounding box is greater than this threshold. | float|None |
|
None |
text_det_unclip_ratio |
The text detection expansion ratio. The larger this value, the larger the expanded area. | float|None |
|
None |
text_rec_score_thresh |
The text recognition score threshold. Text results with scores greater than this threshold will be retained. | float|None |
|
None |
| Method | Description | Parameter | Parameter Type | Parameter Description | Default Value |
|---|---|---|---|---|---|
print() |
Print the result to the terminal | format_json |
bool |
Whether to format the output content using JSON indentation |
True |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable. This is only effective when format_json is True |
4 | ||
ensure_ascii |
bool |
Control whether non-ASCII characters are escaped to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters. This is only effective when format_json is True |
False |
||
save_to_json() |
Save the result as a JSON file | save_path |
str |
The file path for saving. When a directory is specified, the saved file name will match the input file name | None |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable. This is only effective when format_json is True |
4 | ||
ensure_ascii |
bool |
Control whether non-ASCII characters are escaped to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters. This is only effective when format_json is True |
False |
||
save_to_img() |
Save the result as an image file | save_path |
str |
The file path for saving, supporting both directory and file paths | None |
| Attribute | Description |
|---|---|
json |
Get the prediction results in json format |
img |
Get the visualization image in dict format |
API Reference
For primary operations provided by the service:
- The HTTP request method is POST.
- Both the request body and response body are JSON data (JSON objects).
- When the request is processed successfully, the response status code is
200, and the properties of the response body are as follows:
| Name | Type | Meaning |
|---|---|---|
logId |
string |
The UUID of the request. |
errorCode |
integer |
Error code. Fixed as 0. |
errorMsg |
string |
Error message. Fixed as "Success". |
result |
object |
The result of the operation. |
- When the request is not processed successfully, the properties of the response body are as follows:
| Name | Type | Meaning |
|---|---|---|
logId |
string |
The UUID of the request. |
errorCode |
integer |
Error code. Same as the response status code. |
errorMsg |
string |
Error message. |
Primary operations provided by the service:
infer
Get the OCR result of an image.
POST /ocr
- The properties of the request body are as follows:
| Name | Type | Meaning | Required |
|---|---|---|---|
file |
string |
The URL of an image file or PDF file accessible by the server, or the Base64 encoded result of the content of the above-mentioned file types. For PDF files with more than 10 pages, only the content of the first 10 pages will be used. | Yes |
fileType |
integer |
File type. 0 indicates a PDF file, and 1 indicates an image file. If this property is not present in the request body, the file type will be inferred based on the URL. |
No |
inferenceParams |
object |
Inference parameters. | No |
The properties of inferenceParams are as follows:
| Name | Type | Meaning | Required |
|---|---|---|---|
maxLongSide |
integer |
If the longer side of the input image for the text detection model exceeds maxLongSide during inference, the image will be scaled down so that its longer side equals maxLongSide. |
No |
- When the request is processed successfully, the
resultin the response body has the following properties:
| Name | Type | Meaning | ocrResults |
array |
OCR results. The array length is 1 (for image input) or the smaller of the number of document pages and 10 (for PDF input). For PDF input, each element in the array represents the processing result of each page in the PDF file. |
|---|---|---|
dataInfo |
object |
Information about the input data. |
Each element in ocrResults is an object with the following properties:
| Name | Type | Description |
|---|---|---|
texts |
array |
Text locations, content, and scores. |
image |
string |
The OCR result image with annotated text locations. The image is in JPEG format and Base64-encoded. |
Each element in texts is an object with the following properties:
| Name | Type | Meaning |
|---|---|---|
poly |
array |
Text location. The elements in the array are the coordinates of the vertices of the polygon surrounding the text. |
text |
string |
Text content. |
score |
number |
Text recognition score. |
Multi-language Service Call Examples
Python
import base64
import requests
API_URL = "http://localhost:8080/ocr"
file_path = "./demo.jpg"
with open(file_path, "rb") as file:
file_bytes = file.read()
file_data = base64.b64encode(file_bytes).decode("ascii")
payload = {"file": file_data, "fileType": 1}
# Call the API
response = requests.post(API_URL, json=payload)
# Process the response data
assert response.status_code == 200
result = response.json()["result"]
for i, res in enumerate(result["ocrResults"]):
print("Detected texts:")
print(res["texts"])
output_img_path = f"out_{i}.jpg"
with open(output_img_path, "wb") as f:
f.write(base64.b64decode(res["image"]))
print(f"Output image saved at {output_img_path}")
📱 Edge Deployment: Edge deployment is a method of placing computing and data processing capabilities directly on user devices, allowing them to process data locally without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed instructions, please refer to the [PaddleX Edge Deployment Guide](../../../pipeline_deploy/edge_deploy.en.md). You can choose the appropriate deployment method based on your needs to integrate the model production line into your AI applications. ## 4. Secondary Development If the default model weights provided by the General OCR production line do not meet your requirements in terms of accuracy or speed, you can attempt to fine-tune the existing models using your own domain-specific or application-specific data to improve the recognition performance of the General OCR production line in your scenario. ### 4.1 Model Fine-Tuning Since the General OCR production line consists of several modules, the unsatisfactory performance of the production line may originate from any one of these modules. You can analyze the images with poor recognition results to identify which module is problematic and refer to the corresponding fine-tuning tutorial links in the table below for model fine-tuning.
| Scenario | Fine-Tuning Module | Fine-Tuning Reference Link |
|---|---|---|
| Text is missed in detection | Text Detection Module | Link |
| Text content is inaccurate | Text Recognition Module | Link |
| Vertical or rotated text line correction is inaccurate | Text Line Orientation Classification Module | Link |
| Whole-image rotation correction is inaccurate | Document Image Orientation Classification Module | Link |
| Image distortion correction is inaccurate | Text Image Correction Module | Fine-tuning not supported yet |