Text Image Rectification Module Usage Tutorial¶

1. Overview¶

The primary purpose of text image rectification is to perform geometric transformations on images to correct distortions, inclinations, perspective deformations, etc., in the document images for more accurate subsequent text recognition.

2. Supported Model List¶

The inference time only includes the model inference time and does not include the time for pre- or post-processing. The "Normal Mode" values correspond to the local paddle_static inference engine.

Model	Model Download Link	CER	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (MB)	Description
UVDoc	Inference Model/Training Model	0.179	19.05 / 19.05	- / 869.82	30.3	High-accuracy text image rectification model

Test Environment Description:

Performance Test Environment
- Test Dataset: DocUNet benchmark dataset.
- Hardware Configuration:
  - GPU: NVIDIA Tesla T4
  - CPU: Intel Xeon Gold 6271C @ 2.60GHz
- Software Environment:
  - Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6
  - paddlepaddle-gpu 3.0.0 / paddleocr 3.0.3
Inference Mode Explanation

Mode	GPU Configuration	CPU Configuration	Acceleration Technology Combination
Regular Mode	FP32 Precision / No TRT Acceleration	FP32 Precision / 8 Threads	PaddleInference
High-Performance Mode	Choose the optimal combination of prior precision type and acceleration strategy	FP32 Precision / 8 Threads	Choose the optimal prior backend (Paddle/OpenVINO/TRT, etc.)

3. Quick Start¶

❗ Before starting quickly, please first install the PaddleOCR wheel package. For details, please refer to the installation tutorial.

You can quickly experience it with one command:

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

The example above uses the paddle_static inference engine by default. To run it, first install PaddlePaddle by following PaddlePaddle Framework Installation.

If you choose transformers as the inference engine, make sure the Transformers environment is configured, and then run the following command:

# Use the transformers engine for inference
paddleocr text_image_unwarping -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/doc_test.jpg \
    --engine transformers

In most scenarios, the default paddle_static inference engine delivers better inference performance and is the recommended first choice.

Note: The official models would be download from HuggingFace by default. If can't access to HuggingFace, please set the environment variable PADDLE_PDX_MODEL_SOURCE="BOS" to change the model source to BOS. In the future, more model sources will be supported.

You can also integrate the model inference from the image rectification module into your project. Before running the following code, please download the sample image locally.

from paddleocr import TextImageUnwarping
model = TextImageUnwarping(model_name="UVDoc")
output = model.predict("doc_test.jpg", 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")

The example above uses the paddle_static inference engine by default. To run it, first install PaddlePaddle by following PaddlePaddle Framework Installation.

If you choose transformers as the inference engine, make sure the Transformers environment is configured, and then run the following code:

from paddleocr import TextImageUnwarping
model = TextImageUnwarping(
    model_name="UVDoc",
    engine="transformers",
)
output = model.predict("doc_test.jpg", 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")

In most scenarios, the default paddle_static inference engine delivers better inference performance and is the recommended first choice.

After running, the result obtained is:

{'res': {'input_path': 'doc_test.jpg', 'page_index': None, 'doctr_img': '...'}}

The meanings of the parameters in the result are as follows:

input_path：Indicates the path of the image to be rectified
doctr_img：Indicates the rectified image result. Due to the large amount of data, it is not convenient to print directly, so it is replaced here with....You can useres.save_to_img()to save the prediction result as an image, and res.save_to_json() to save the prediction result as a json file.

The visualized image is as follows:

The relevant methods, parameters, etc., are described as follows:

TextImageUnwarping instantiates the image rectification model (taking UVDoc as an example here), with specific explanations as follows:

Parameter	Description	Type	Default
`model_name`	Meaning: Name of the model	`str`	`None`
`model_dir`	Meaning: Model storage path	`str`	`None`
`device`	Meaning: Device(s) to use for inference. Description: 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`
`engine`	Meaning: Inference engine. Description: Supports `None` (the default), `paddle`, `paddle_static`, `paddle_dynamic`, and `transformers`. When left as `None`, local inference uses the `paddle_static` engine by default. For detailed descriptions, supported values, compatibility rules, and examples, see Inference Engine and Configuration.	`str\|None`	`None`
`engine_config`	Meaning: Inference-engine configuration. Description: Recommended together with `engine`. For supported fields, compatibility rules, and examples, see Inference Engine and Configuration.	`dict\|None`	`None`
`enable_hpi`	Meaning: Whether to use the high performance inference.	`bool`	`False`
`use_tensorrt`	Meaning: Whether to use the Paddle Inference TensorRT subgraph engine. Description: If the model does not support acceleration through TensorRT, setting this flag will not enable acceleration. For Paddle with CUDA version 11.8, the compatible TensorRT version is 8.x (x>=6), and it is recommended to install TensorRT 8.6.1.6.	`bool`	`False`
`precision`	Meaning:Precision for TensorRT when using the Paddle Inference TensorRT subgraph engine. Description: Options: `"fp32"`, `"fp16"`, etc.	`str`	`"fp32"`
`enable_mkldnn`	Meaning: Whether to enable MKL-DNN acceleration for inference. Description: If MKL-DNN is unavailable or the model does not support it, acceleration will not be used even if this flag is set.	`bool`	`True`
`mkldnn_cache_capacity`	Meaning:MKL-DNN cache capacity.	`int`	`10`
`cpu_threads`	Meaning: Number of threads to use for inference on CPUs.	`int`	`10`

Call the predict() method of the image rectification model for inference prediction. This method will return a result list. Additionally, this module also provides a predict_iter() method. Both methods are consistent in terms of parameter acceptance and result return. The difference is that predict_iter() returns a generator, which can process and obtain prediction results step by step, suitable for handling large datasets or scenarios where memory saving is desired. You can choose to use either of these methods according to your actual needs. The predict() method has parameters input and batch_size, with specific explanations as follows:

Parameter	Description	Type	Default
`input`	Meaning: Input data to be predicted. Required. Description: Supports multiple input types: Python Var: e.g., `numpy.ndarray` representing image data str: Local image or PDF file path: `/root/data/img.jpg`; URL of image or PDF file: e.g., example; Local directory: directory containing images for prediction, e.g., `/root/data/` (Note: directories containing PDF files are not supported; PDFs must be specified by exact file path) list: Elements must be of the above types, e.g., `[numpy.ndarray, numpy.ndarray]`, `["/root/data/img1.jpg", "/root/data/img2.jpg"]`, `["/root/data1", "/root/data2"]`	`Python Var\|str\|list`
`batch_size`	Meaning: Batch size Description: Positive integer.	`int`	1

Process the prediction results. The prediction result for each sample is a corresponding Result object, which supports printing, saving as an image, and saving as a json file:

Method	Description	Parameter	Type	Parameter Description	Default Value
`print()`	Print result to terminal	`format_json`	`bool`	Whether to format the output content using `JSON` indentation	`True`
		`indent`	`int`	Specifies the indentation level to beautify the output `JSON` data, making it more readable, effective only when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Controls whether to escape non-`ASCII` characters into `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` will retain the original characters, effective only when `format_json` is `True`	`False`
`save_to_json()`	Save the result as a json format file	`save_path`	`str`	The path to save the file. When specified as a directory, the saved file is named consistent with the input file type.	None
		`indent`	`int`	Specifies the indentation level to beautify the output `JSON` data, making it more readable, effective only when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Controls whether to escape non-`ASCII` characters into `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` will retain the original characters, effective only when `format_json` is `True`	`False`
`save_to_img()`	Save the result as an image format file	`save_path`	`str`	The path to save the file. When specified as a directory, the saved file is named consistent with the input file type.	None

Additionally, the result can be obtained through attributes that provide the visualized images with results and the prediction results, as follows:

Attribute	Description
`json`	Get the prediction result in `json` format
`img`	Get the visualized image in `dict` format

4. Secondary Development¶

The current module does not support fine-tuning training and only supports inference integration. Concerning fine-tuning training for this module, there are plans to support it in the future.

5. Inference Engine¶

For detailed descriptions, values, compatibility rules, and examples of the inference engine, please refer to Inference Engine and Configuration Description.

5.1 Speed Data¶

model	engine	Preprocessing (ms)	Inference (ms)	PostProcessing (ms)	End-to-End (ms)
UVDoc	paddle_static	14.96	18.60	1.93	36.66
	paddle_dynamic	10.90	27.59	1.96	40.94
	transformers	13.54	6.74	0.91	33.07