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:
| Model | Recognition Avg Accuracy(%) | Model Storage Size (M) | Introduction |
|---|---|---|---|
| PP-LCNet_x1_0_textline_ori | 98.85 | 0.96 | Text line classification model based on PP-LCNet_x0_25, with two classes: 0 degrees and 180 degrees |
Model Usage
Install Dependencies
pip install -U paddleocr
pip install -U onnxruntime-gpu
CLI Usage
paddleocr textline_orientation_classification -i ./demo.jpg --model_name PP-LCNet_x1_0_textline_ori --engine onnxruntime
Python API Usage
from paddleocr import TextLineOrientationClassification
model = TextLineOrientationClassification(
model_name="PP-LCNet_x1_0_textline_ori",
engine="onnxruntime",
)
output = model.predict("./demo.jpg", batch_size=1)
for res in output:
res.print()
res.save_to_json(save_path="./output/res.json")