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*This model was contributed to Hugging Face Transformers on 2026-05-19.*

# PP-OCRv6_tiny_rec

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

PP-OCRv6_tiny_rec is the most lightweight recognition model in the PP-OCRv6 series. It uses LCNetV4 as the backbone with a direct reshape projection instead of an encoder neck, and a CTC+NRTR multi-head decoder. The model supports 49 languages and contains 1.1M parameters.

## Model Architecture

<img src="https://cdn-uploads.huggingface.co/production/uploads/684ba591e717a30275a1b76a/9hBYIA-pO6jhnG6bcZEaf.png" width="600"/>

## Usage

### Single input inference

The example below demonstrates how to detect text with PP-OCRv6_tiny_rec using the [`AutoModel`].

<hfoptions id="usage">
<hfoption id="AutoModel">

```python
from io import BytesIO

import httpx
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForTextRecognition
from transformers.image_utils import load_image

model_path = "PaddlePaddle/PP-OCRv6_tiny_rec_safetensors"
model = AutoModelForTextRecognition.from_pretrained(model_path, device_map="auto")
image_processor = AutoImageProcessor.from_pretrained(model_path)

image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_rec_001.png"
image = load_image(image_url)
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)
```

</hfoption>
</hfoptions>

### Batched inference

Here is how you can do it with PP-OCRv6_tiny_rec using the [`AutoModel`]:

<hfoptions id="usage">
<hfoption id="AutoModel">

```python
from io import BytesIO

import httpx
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForTextRecognition
from transformers.image_utils import load_image

model_path = "PaddlePaddle/PP-OCRv6_tiny_rec_safetensors"
model = AutoModelForTextRecognition.from_pretrained(model_path, device_map="auto")
image_processor = AutoImageProcessor.from_pretrained(model_path)

image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_rec_001.png"
image = load_image(image_url)
inputs = image_processor(images=[image, image], return_tensors="pt").to(model.device)
outputs = model(**inputs)

results = image_processor.post_process_text_recognition(outputs)
for result in results:
    print(result)
```

</hfoption>
</hfoptions>

## PPOCRV6TinyRecForTextRecognition

[[autodoc]] PPOCRV6TinyRecForTextRecognition

## PPOCRV6TinyRecConfig

[[autodoc]] PPOCRV6TinyRecConfig

## PPOCRV6TinyRecModel

[[autodoc]] PPOCRV6TinyRecModel

## PPOCRV6SmallRecImageProcessor

[[autodoc]] PPOCRV6SmallRecImageProcessor
