PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks

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PP-OCRv6 Overview

PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge.

Key Features

Unified and Scalable Model Family: A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6_medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5_server by +4.6% and +5.1% respectively.
Lightweight Architectural Innovations: (i) LCNetV4, a MetaFormer-style lightweight backbone with structural reparameterization; (ii) RepLKFPN, a detection neck with dilated reparameterizable depthwise convolutions; (iii) EncoderWithLightSVTR, a recognition neck with local-global attention and additive skip connections.
Multi-Language and Scenario Support: Supports 50 languages and diverse industrial scenes (digital displays, dot-matrix characters, tire prints, etc.), surpassing Qwen3-VL-235B, GPT-5.5, and Gemini-3.1-Pro with orders of magnitude fewer parameters.

PP-OCRv6_medium_rec

Introduction

PP-OCRv6 text recognition architecture overview

PP-OCRv6_small_rec is the mid-tier recognition model in the PP-OCRv6 series. It uses LCNetV4 as the backbone and EncoderWithLightSVTR as the recognition neck, with a CTC+NRTR multi-head decoder. The model supports 50 languages and contains 5.2M parameters. The key accuracy metrics are as follows:

Model	W-Avg	Handwritten CN	Handwritten EN	Printed CN	Printed EN	TC	Ancient	JP	Confusable	Special	General	Pinyin	Artistic	Industrial	Screen	Card
GPT-5.5	64.2	19.2	56.9	75.7	82.2	57.5	63.7	58.6	49.1	48.3	67.7	50.4	53.0	62.4	67.7	71.1
Qwen3-VL-235B	74.9	49.7	73.2	82.3	86.2	76.4	33.6	66.2	56.1	49.0	82.5	76.5	69.6	74.7	73.8	78.7
Kimi-K2.6	62.9	31.0	58.4	76.8	80.9	62.7	16.5	54.1	43.5	38.0	68.0	45.2	59.9	57.1	58.4	68.4
MiniMax-M3	54.1	15.5	60.3	63.5	81.5	53.2	2.2	43.7	42.2	42.8	53.8	50.3	44.3	44.1	56.6	67.0
Gemini-3.1-Pro	71.4	46.4	73.0	80.0	90.5	69.5	18.0	67.2	54.4	50.3	74.6	75.9	63.1	69.1	73.2	75.9
PP-OCRv5_server	78.1	58.0	59.6	90.1	85.1	74.7	60.4	73.7	59.4	56.8	86.5	74.4	64.0	70.2	68.1	87.6
PP-OCRv5_mobile	73.7	41.7	50.9	86.0	86.0	72.0	57.8	75.8	55.7	54.8	80.7	72.5	54.0	59.3	57.6	81.7
PP-OCRv6_medium	83.2	62.1	67.8	91.5	94.1	78.6	72.4	90.5	64.9	61.7	87.5	78.1	71.2	77.4	82.5	88.1
PP-OCRv6_small	81.3	57.6	61.1	90.5	93.3	77.0	71.1	88.2	64.1	60.2	85.7	75.9	68.4	76.4	79.7	86.9
PP-OCRv6_tiny	73.5	40.1	39.3	86.7	88.4	65.0	68.4	89.8	52.3	57.1	78.0	65.4	54.7	62.1	71.2	80.5

Quick Start

Installation

PaddleOCR

# Install the basic version
pip install paddleocr

# Install the full version (includes all features)
pip install "paddleocr[all]"

ONNX Runtime environment (required for onnx models)

pip install onnxruntime-gpu

Model Usage

You can quickly experience the functionality with a single command:

paddleocr text_recognition \
    --model_name PP-OCRv6_medium_rec \
    --engine onnxruntime \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/2PZfbirjfxA88695lRmgk.jpeg

You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine.

from paddleocr import TextRecognition
model = TextRecognition(model_name="PP-OCRv6_medium_rec", engine="onnxruntime")
output = model.predict(input="2PZfbirjfxA88695lRmgk.jpeg", batch_size=1)
for res in output:
    res.print()
    res.save_to_json(save_path="./output/res.json")

After running, the obtained result is as follows:

{'res': {'input_path': '2PZfbirjfxA88695lRmgk.jpeg', 'page_index': None, 'rec_text': 'day as a reminder of the', 'rec_score': 0.9857}}

The visualized image is as follows:

For details about usage command and descriptions of parameters, please refer to the Document.

Pipeline Usage

The general OCR pipeline is used to solve text recognition tasks by extracting text information from images. The pipeline consists of several modules:

Document Image Orientation Classification Module (Optional)
Text Image Unwarping Module (Optional)
Text Line Orientation Classification Module (Optional)
Text Detection Module
Text Recognition Module

Run a single command to quickly experience the OCR pipeline:

paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \
    --text_detection_model_name PP-OCRv6_medium_det \
    --text_recognition_model_name PP-OCRv6_medium_rec \
    --engine onnxruntime \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --use_textline_orientation True \
    --save_path ./output \
    --device gpu:0

If save_path is specified, the visualization results will be saved under save_path. The visualization output is shown below:

For project integration:

from paddleocr import PaddleOCR

ocr = PaddleOCR(
    text_detection_model_name="PP-OCRv6_medium_det",
    text_recognition_model_name="PP-OCRv6_medium_rec",
    engine="onnxruntime",
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
    use_textline_orientation=True,
)
result = ocr.predict("./3ul2Rq4Sk5Cn-l69D695U.png")
for res in result:
    res.print()
    res.save_to_img("output")
    res.save_to_json("output")