UVDoc Safetensors
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, so that the...
Model source
- Kind
- Other
- File size
- 30.2 MB
- Version
- vRevision bfa6aad
- Updated
- Oct 15, 2026
- Downloads
- 159
- Source
- Hugging Face
Source description
UVDoc
Introduction
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, so that the subsequent text recognition can be more accurate.
| Model | CER |
|---|---|
| UVDoc | 0.179 |
Note: Test data set: docunet benchmark data set.
Model Usage
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)
Sources
1 source
Sources
1 sourceVerified Aug 27
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - doc_img_unwarping --- # UVDoc ## Introduction 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, so that the subsequent text recognition can be more accurate. | Model| CER | | --- | --- | |UVDoc | 0.179 | **Note**: Test data set: docunet benchmark data set. ## Model Usage ```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) ```
Source context: 159 downloads · 3 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/UVDoc_safetensors
