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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...

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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.

ModelCER
UVDoc0.179

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
Hugging FacePrimaryVersion bfa6aad03b549c7e2cf89cefd2fb02ba3bef2ae4
huggingface.co/PaddlePaddle/UVDoc_safetensors/tree/bfa6aad03b549c7e2cf89cefd2fb02ba3bef2ae4

Verified Aug 27

Model artifacts

1 artifact

model.safetensors

safetensors · 30.2 MB · SHA-256 65f980e3083b…25dc · Hugging Face

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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