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Text Line Orientation Classification Module Tutorial

1. Overview

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.

2. Supported Model List

ModelModel Download Link Top-1 Accuracy (%) GPU Inference Time (ms)
[Normal Mode / High-Performance Mode]		 CPU Inference Time (ms) Model Size (M) Description
PP-LCNet_x0_25_textline_oriInference Model/Training Model 98.85 - - 0.96 Text line classification model based on PP-LCNet_x0_25, with two classes: 0 degrees and 180 degrees
PP-LCNet_x1_0_textline_oriInference Model/Training Model 99.42 - - 6.5 Text line classification model based on PP-LCNet_x1_0, with two classes: 0 degrees and 180 degrees

❗ Note: The text line orientation classification model was upgraded on May 26, 2025, and PP-LCNet_x1_0_textline_ori has been added. If you need to use the pre-upgrade model weights, please click the download link.

Test Environment Description:

  • Performance Test Environment
    • Test Dataset： PaddleX Self-built Dataset, Covering Multiple Scenarios Such as Documents and Certificates, Containing 1000 Images.
    • Hardware Configuration：
      • GPU: NVIDIA Tesla T4
      • CPU: Intel Xeon Gold 6271C @ 2.60GHz
      • Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
  • Inference Mode Description
Mode GPU Configuration CPU Configuration Acceleration Technology Combination
Normal Mode FP32 Precision / No TRT Acceleration FP32 Precision / 8 Threads PaddleInference
High-Performance Mode Optimal combination of pre-selected precision types and acceleration strategies FP32 Precision / 8 Threads Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.)

3. Quick Integration

❗ Before starting, please install the wheel package of PaddleOCR. For detailed instructions, refer to the Installation Guide.

You can quickly experience the functionality with a single command:

paddleocr textline_orientation_classification -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/textline_rot180_demo.jpg

You can also integrate the text line orientation classification model into your project. Run the following code after downloading the example image to your local machine. 

from paddleocr import TextLineOrientationClassification
model = TextLineOrientationClassification(model_name="PP-LCNet_x0_25_textline_ori")
output = model.predict("textline_rot180_demo.jpg",  batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_img("./output/demo.png")
    res.save_to_json("./output/res.json")

After running, the result obtained is:

{'res': {'input_path': 'textline_rot180_demo.jpg', 'page_index': None, 'class_ids': array([1], dtype=int32), 'scores': array([0.99864], dtype=float32), 'label_names': ['180_degree']}}

The meanings of the running results parameters are as follows:

  • input_path：Indicates the path of the input image.
  • page_index：If the input is a PDF file, it indicates the current page number of the PDF; otherwise, it is None.
  • class_ids：Indicates the class ID of the prediction result.
  • scores：Indicates the confidence score of the prediction result.
  • label_names：Indicates the class name of the prediction result. The visualization image is as follows:

The explanations for the methods, parameters, etc., are as follows:

  • TextLineOrientationClassification instantiates a textline classification model (here, PP-LCNet_x0_25_textline_ori is used as an example), and the specific explanations are as follows:
Parameter Description Type Default
model_name Name of the model str None
model_dir Model storage path str None
device Device(s) to use for inference.
Examples: cpu, gpu, npu, gpu:0, gpu:0,1.
If multiple devices are specified, inference will be performed in parallel. Note that parallel inference is not always supported.
By default, GPU 0 will be used if available; otherwise, the CPU will be used. 		str None
enable_hpi Whether to use the high performance inference. bool False
use_tensorrt Whether to use the Paddle Inference TensorRT subgraph engine. bool False
min_subgraph_size Minimum subgraph size for TensorRT when using the Paddle Inference TensorRT subgraph engine. int 3
precision Precision for TensorRT when using the Paddle Inference TensorRT subgraph engine.
Options: fp32, fp16, etc.		 str fp32
enable_mkldnn Whether to use MKL-DNN acceleration for inference. bool True
cpu_threads Number of threads to use for inference on CPUs. int 10
top_k The top-k value for prediction results. If not specified, the default value in the official PaddleOCR model configuration is used. If the value is 5, the top 5 categories and their corresponding classification probabilities will be returned. int None

  • model_name must be specified. Once model_name is set, the default built-in model parameters of PaddleOCR will be used. On this basis, if model_dir is specified, the user-defined model will be used.

  • Use the predict() method of the text line direction classification model to perform inference. This method returns a list of results. In addition, this module also provides the predict_iter() method. Both methods accept the same parameters and return the same result format. The difference is that predict_iter() returns a generator, which processes and retrieves prediction results step by step. It is suitable for handling large datasets or memory-efficient scenarios. You can choose either method based on your actual needs. The predict() method accepts the parameters input and batch_size, which are described in detail below:

Parameter Description Type Default
input Input data for prediction. Multiple input types are supported. This parameter is required.
  • Python Var: such as numpy.ndarray representing image data
  • str: such as the local path of an image or PDF file: /root/data/img.jpg; or a URL link, such as the online URL of an image or PDF file: Example; or a local directory that contains images for prediction, such as /root/data/ (currently, directories containing PDF files are not supported; PDF files must be specified as individual file paths)
  • List: list elements must be of the above types, such as [numpy.ndarray, numpy.ndarray], ["/root/data/img1.jpg", "/root/data/img2.jpg"], ["/root/data1", "/root/data2"]
 Python Var|str|list
batch_size Batch size, positive integer. int 1
  • Call the predict() method of the text line orientation classification model for inference. This method will return a list of results. In addition, this module also provides a predict_iter() method. Both methods accept the same parameters and return the same results, but predict_iter() returns a generator, which is more suitable for processing large datasets or when you want to save memory. You can choose either method according to your needs. The parameters of the predict() method are input and batch_size, as described below:
Parameter Parameter Description Parameter Type Options Default Value
input Data to be predicted, supporting multiple input types Python Var/str/list
  • Python variable, such as image data represented by numpy.ndarray
  • File path, such as the local path of an image file: /root/data/img.jpg
  • URL link, such as the network URL of an image file: Example
  • Local directory, the directory should contain data files to be predicted, such as the local path: /root/data/
  • List, the elements of the list should be of the above-mentioned data types, such as [numpy.ndarray, numpy.ndarray], [\"/root/data/img1.jpg\", \"/root/data/img2.jpg\"], [\"/root/data1\", \"/root/data2\"]
 None
batch_size Batch size int Any integer 1
  • The prediction results are processed, and the prediction result for each sample is of type dict. It supports operations such as printing, saving as an image, and saving as a json file:
Method Method Description Parameter Parameter Type Parameter Description Default Value
print() Print the results to the terminal format_json bool Whether to format the output content using JSON indentation True
indent int Specify the indentation level to beautify the output JSON data, making it more readable, only effective when format_json is True 4
ensure_ascii bool Control whether to escape non-ASCII characters to Unicode. If set to True, all non-ASCII characters will be escaped; False retains the original characters, only effective when format_json is True False
save_to_json() Save the results as a JSON file save_path str The path to save the file. If it is a directory, the saved file name will be consistent with the input file name None
indent int Specify the indentation level to beautify the output JSON data, making it more readable, only effective when format_json is True 4
ensure_ascii bool Control whether to escape non-ASCII characters to Unicode. If set to True, all non-ASCII characters will be escaped; False retains the original characters, only effective when format_json is True False
save_to_img() Save the results as an image file save_path str The path to save the file. If it is a directory, the saved file name will be consistent with the input file name None
  • Additionally, it supports obtaining the visualization image with results and the prediction results through attributes, as follows:
Attribute Attribute Description
json Get the prediction result in json format
img Get the visualization image in dict format

4. Custom Development

Since PaddleOCR does not natively support training for text line orientation classification, refer to PaddleX's Custom Development Guide for training. Trained models can seamlessly integrate into PaddleOCR's API for inference.

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