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*This model was released on 2023-02-06 and added to Hugging Face Transformers on 2026-03-19.*

# UVDoc

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

**UVDoc** The main purpose of text image correction is to carry out geometric transformation on the image to correct the document distortion, inclination, perspective deformation and other problems in the image.

## Usage

### Single input inference

The example below demonstrates how to rectify a document image with UVDoc using the [`AutoImageProcessor`] and [`UVDocModel`].

<hfoptions id="usage">
<hfoption id="AutoModel">

```py
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModel

model_path = "PaddlePaddle/UVDoc_safetensors"
model = AutoModel.from_pretrained(
    model_path,
    device_map="auto",
)
image_processor = AutoImageProcessor.from_pretrained(model_path)

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/doc_test.jpg", stream=True).raw)

inputs = image_processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)

result = image_processor.post_process_document_rectification(outputs.last_hidden_state, inputs["original_images"])
print(result)
```

</hfoption>
</hfoptions>

### Batched inference

Here is how to perform batched document rectification with UVDoc:

<hfoptions id="usage">
<hfoption id="AutoModel">

```py
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModel

model_path = "PaddlePaddle/UVDoc_safetensors"
model = AutoModel.from_pretrained(
    model_path
    device_map="auto",
)
image_processor = AutoImageProcessor.from_pretrained(model_path)

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/doc_test.jpg", stream=True).raw)

inputs = image_processor(images=[image, image], return_tensors="pt").to(model.device)
outputs = model(**inputs)

result = image_processor.post_process_document_rectification(outputs.last_hidden_state, inputs["original_images"])
print(result)
```

</hfoption>
</hfoptions>

## UVDocConfig

[[autodoc]] UVDocConfig

## UVDocModel

[[autodoc]] UVDocModel

## UVDocBackboneConfig

[[autodoc]] UVDocBackboneConfig

## UVDocBackbone

[[autodoc]] UVDocBackbone

## UVDocBridge

[[autodoc]] UVDocBridge

## UVDocImageProcessor

[[autodoc]] UVDocImageProcessor
