Aller au contenu
ModelPrésentéExterne
Télécharger

UVDoc Onnx

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...

TypeModèleVersionRevision 3bcf535BClasse

Source du modèle

Type
Modèle
Taille du fichier
30,2 MB
Version
vRevision 3bcf535
Mis à jour
30 oct. 2026
Téléchargements
1,9 k
Source
Hugging Face

Description de la source

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.

ModelCER
UVDoc0.179

Note: Test data set: docunet benchmark data set.

Model Usage

Install Dependencies

pip install -U paddleocr
pip install -U onnxruntime-gpu

CLI Usage

paddleocr text_image_unwarping -i ./demo.jpg --model_name UVDoc --engine onnxruntime

Python API Usage

from paddleocr import TextImageUnwarping

model = TextImageUnwarping(
    model_name="UVDoc",
    engine="onnxruntime",
)
output = model.predict("./demo.jpg", batch_size=1)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")

Sources

1 source
Hugging FacePrincipalVersion 3bcf535371727d11e783101f79a504c68848aae3
huggingface.co/PaddlePaddle/UVDoc_onnx/tree/3bcf535371727d11e783101f79a504c68848aae3

Vérifié 30 oct.

Artefacts du modèle

1 artefact

inference.onnx

onnx · 30,2 MB · SHA-256 54cab30dc2cf…63d6 · Hugging Face

Download file

Extraits de sources

3 extraits

--- 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 ### Install Dependencies ```shell pip install -U paddleocr pip install -U onnxruntime-gpu ``` ### CLI Usage ```shell paddleocr text_image_unwarping -i ./demo.jpg --model_name UVDoc --engine onnxruntime ``` ### Python API Usage ```python from paddleocr import TextImageUnwarping model = TextImageUnwarping( model_name="UVDoc", engine="onnxruntime", ) output = model.predict("./demo.jpg", batch_size=1) for res in output: res.print() res.save_to_img(save_path="./output/") ```

Source context: 1850 downloads · 2 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/UVDoc_onnx

Source context: 484 downloads · 0 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/UVDoc_onnx