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...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 30,2 MB
- Versão
- vRevision bfa6aad
- Atualizado
- 15 de out. de 2026
- Downloads
- 159
- Fonte
- Hugging Face
Descrição da fonte
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)
Fontes
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Fontes
1 fonteVerificado 27 de ago.
Artefatos de modelo
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Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- 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
