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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...

TypeOtherVersionRevision 542979dBClasse

Source du modèle

Type
Other
Taille du fichier
80,5 MB
Version
vRevision 542979d
Mis à jour
14 oct. 2026
Téléchargements
232
Source
Hugging Face

Description de la source

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)

Sources

1 source
Hugging FacePrincipalVersion 542979d7cc3791732bb12af35313a6840952d79f
huggingface.co/PaddlePaddle/PP-OCRv5_server_rec_safetensors/tree/542979d7cc3791732bb12af35313a6840952d79f

Vérifié 27 août

Artefacts du modèle

1 artefact

model.safetensors

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

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Extraits de sources

2 extraits

--- 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