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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+-->
+*This model was contributed to Hugging Face Transformers on 2026-03-21.*
+
+# UVDoc
+
+
+## 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