Ir para o conteúdo
ModelListadosExterno
Baixar

PP OCRv5 Server Rec Safetensors

PP-OCRv5serverrec is one of the PP-OCRv5rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of four major...

TipoOtherVersãoRevision 542979dBnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
80,5 MB
Versão
vRevision 542979d
Atualizado
14 de out. de 2026
Downloads
232
Fonte
Hugging Face

Descrição da fonte

PP-OCRv5_server_rec

Introduction

PP-OCRv5_server_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—as well as complex text scenarios such as handwriting, vertical text, pinyin, and rare characters using a single model. The key accuracy metrics are as follow:

Handwritten ChineseHandwritten EnglishPrinted ChinesePrinted EnglishTraditional ChineseAncient TextJapaneseGeneral ScenarioPinyinRotationDistortionArtistic TextAverage
0.58070.58060.90130.86790.74720.60390.73720.59460.83840.74350.93140.63970.8401

Note: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications.

Model Usage

import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForTextRecognition

model_path="PaddlePaddle/PP-OCRv5_server_rec_safetensors"
model = AutoModelForTextRecognition.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/general_ocr_rec_001.png", stream=True).raw).convert("RGB")
inputs = image_processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)

results = image_processor.post_process_text_recognition(outputs)

for result in results:
    print(result)

Fontes

1 fonte
Hugging FacePrimárioVersão 542979d7cc3791732bb12af35313a6840952d79f
huggingface.co/PaddlePaddle/PP-OCRv5_server_rec_safetensors/tree/542979d7cc3791732bb12af35313a6840952d79f

Verificado 27 de ago.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 80,5 MB · SHA-256 da94c5dd42c8…aa91 · Hugging Face

Baixar

Trechos de fonte

2 trechos

--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - textline_recognition --- # PP-OCRv5_server_rec ## Introduction PP-OCRv5_server_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—as well as complex text scenarios such as handwriting, vertical text, pinyin, and rare characters using a single model. The key accuracy metrics are as follow: | Handwritten Chinese | Handwritten English | Printed Chinese | Printed English | Traditional Chinese | Ancient Text | Japanese | General Scenario | Pinyin | Rotation | Distortion | Artistic Text | Average | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0.5807 | 0.5806 | 0.9013 | 0.8679 | 0.7472 | 0.6039 | 0.7372 | 0.5946 | 0.8384 | 0.7435 | 0.9314 | 0.6397 | 0.8401 | **Note**: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. ## Model Usage ```python import requests from PIL import Image from transformers import AutoImageProcessor, AutoModelForTextRecognition model_path="PaddlePaddle/PP-OCRv5_server_rec_safetensors" model = AutoModelForTextRecognition.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/general_ocr_rec_001.png", stream=True).raw).convert("RGB") inputs = image_processor(images=image, return_tensors="pt").to(model.device) outputs = model(**inputs) results = image_processor.post_process_text_recognition(outputs) for result in results: print(result) ```

Source context: 232 downloads · 3 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/PP-OCRv5_server_rec_safetensors