Text Image Unwarping Module Development Tutorial¶
I. Overview¶
The primary purpose of Text Image Unwarping is to perform geometric transformations on images in order to correct issues such as document distortion, tilt, perspective deformation, etc., enabling more accurate recognition by subsequent text recognition modules.
II. Supported Model List¶
| Model Name | Model Download Link | MS-SSIM （%） | Model Size (M) | information |
|---|---|---|---|---|
| UVDoc | Inference Model/Trained Model | 54.40 | 30.3 M | High-precision Text Image Unwarping Model |
The accuracy metrics of the above models are measured on the DocUNet benchmark dataset.
III. Quick Integration¶
❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the PaddleX Local Installation Guide
Just a few lines of code can complete the inference of the Text Image Unwarping module, allowing you to easily switch between models under this module. You can also integrate the model inference of the the Text Image Unwarping module into your project.
Before running the following code, please download the demo image to your local machine.
from paddlex import create_model
model = create_model("UVDoc")
output = model.predict("doc_test.jpg", batch_size=1)
for res in output:
res.print(json_format=False)
res.save_to_img("./output/")
res.save_to_json("./output/res.json")
IV. Custom Development¶
The current module temporarily does not support fine-tuning training and only supports inference integration. Fine-tuning training for this module is planned to be supported in the future.