PP-LCNet_x1_0_textline_ori

Introduction

The text line orientation classification module primarily distinguishes the orientation of text lines and corrects them using post-processing. In processes such as document scanning and license/certificate photography, to capture clearer images, the capture device may be rotated, resulting in text lines in various orientations. Standard OCR pipelines cannot handle such data well. By utilizing image classification technology, the orientation of text lines can be predetermined and adjusted, thereby enhancing the accuracy of OCR processing. The key accuracy metrics are as follow:

Quick Start

Installation

1. PaddlePaddle

Please refer to the following commands to install PaddlePaddle using pip:

# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

# for CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

# for CPU
python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.

2. PaddleOCR

Install the latest version of the PaddleOCR inference package from PyPI:

python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

paddleocr text_line_orientation_classification \
    --model_name PP-LCNet_x1_0_textline_ori \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/m3ZmUPAnst1f9xXvTVLKS.png

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 TextLineOrientationClassification
model = TextLineOrientationClassification(model_name="PP-LCNet_x1_0_textline_ori")
output = model.predict(input="m3ZmUPAnst1f9xXvTVLKS.png", batch_size=1)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")

After running, the obtained result is as follows:

{'res': {'input_path': '/root/.paddlex/predict_input/m3ZmUPAnst1f9xXvTVLKS.png', 'page_index': None, 'class_ids': array([1], dtype=int32), 'scores': array([0.99829], dtype=float32), 'label_names': ['180_degree']}}

The visualized image is as follows:

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

Links

PaddleOCR Repo

PaddleOCR Documentation