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UVDoc

docs/source/en/model_doc/uvdoc.md

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This model was released on 2023-02-06 and added to Hugging Face Transformers on 2026-03-21.

UVDoc

<div class="flex flex-wrap space-x-1"> </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">
python
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