mindocr/tools/infer/text

• MindOCR Online Inference
• • Dependency and Installation
  • Text Detection
  • • Supported Detection Algorithms and Networks
  • Text Recognition
  • • Supported Recognition Algorithms and Networks
  • Text Detection and Recognition Concatenation
  • • Evaluation of the Inference Results
  • Argument List
  • Developer Guide - How to Add a New Model for Inference
  • • Preprocessing
    • Network Inference
    • Postproprocess

MindOCR Online Inference

About Online Inference: Online inference is to infer based on the native MindSpore framework by loading the model checkpoint file then running prediction with MindSpore APIs.

Compared to offline inference (which is implemented in deploy/py_infer in MindOCR), online inferece does not require model conversion for target platforms and can run directly on the training devices (e.g. Ascend 910). But it requires installing the heavy AI framework and the model is not optimized for deployment.

Thus, online inference is more suitable for demonstration and to visually evaluate model generalization ability on unseen data.

Dependency and Installation

Environment	Version
MindSpore	>=1.9
Python	>=3.7

Supported platforms: Linux, MacOS, Windows (Not tested)

Supported devices: CPU, GPU, and Ascend.

Please clone MindOCR at first

git clone https://github.com/mindspore-lab/mindocr.git

Then install the dependency by

pip install -r requirements.txt

For MindSpore(>=1.9) installation, please follow the official installation instructions for the best fit of your machine.

Text Detection

To run text detection on an input image or a directory containing multiple images, please execute

python tools/infer/text/predict_det.py  --image_dir {path_to_img or dir_to_imgs} --det_algorithm DB++

After running, the inference results will be saved in {args.draw_img_save_dir}/det_results.txt, where --draw_img_save_dir is the directory for saving results and is set to ./inference_results by default Here are some results for examples.

Example 1:

Visualization of text detection result on img_108.jpg

, where the saved txt file is as follows

img_108.jpg	[[[228, 440], [403, 413], [406, 433], [231, 459]], [[282, 280], [493, 252], [499, 293], [288, 321]], [[500, 253], [636, 232], [641, 269], [505, 289]], ...]

Example 2:

Visualization of text detection result on paper_sam.png

, where the saved txt file is as follows

paper_sam.png	[[[1161, 340], [1277, 340], [1277, 378], [1161, 378]], [[895, 335], [1152, 340], [1152, 382], [894, 378]], ...]

Notes:

• For input images with high resolution, please set --det_limit_side_len larger, e.g., 1280. --det_limit_type can be set as "min" or "max", where "min " means limiting the image size to be at least --det_limit_side_len, "max" means limiting the image size to be at most --det_limit_side_len.

• For more argument illustrations and usage, please run python tools/infer/text/predict_det.py -h or view tools/infer/text/config.py

• Currently, this script runs serially to avoid dynamic shape issue and achieve better performance.

Supported Detection Algorithms and Networks

Algorithm Name	Network Name	Language
DB	dbnet_resnet50	English
DB++	dbnetpp_resnet50	English
DB_MV3	dbnet_mobilenetv3	English
PSE	psenet_resnet152	English

The algorithm-network mapping is defined in tools/infer/text/predict_det.py.

Text Recognition

To run text recognition on an input image or a directory containing multiple images, please execute

python tools/infer/text/predict_rec.py  --image_dir {path_to_img or dir_to_imgs} --rec_algorithm CRNN

After running, the inference results will be saved in {args.draw_img_save_dir}/rec_results.txt, where --draw_img_save_dir is the directory for saving results and is set to ./inference_results by default. Here are some results for examples.

• English text recognition

word_1216.png

word_1217.png

Recognition results:

word_1216.png   coffee
word_1217.png   club

• Chinese text recognition:

cert_id.png

doc_cn3.png

Recognition results:

cert_id.png 公民身份号码44052419
doc_cn3.png 马拉松选手不会为短暂的领先感到满意，而是永远在奔跑。

Notes:

• For more argument illustrations and usage, please run python tools/infer/text/predict_rec.py -h or view tools/infer/text/config.py
• Both batch-wise and single-mode inference are supported. Batch mode is enabled by default for better speed. You can set the batch size via --rec_batch_size. You can also run in single-mode by set --det_batch_mode False, which may improve accuracy if the text length varies a lot.

Supported Recognition Algorithms and Networks

Algorithm Name	Network Name	Language
CRNN	crnn_resnet34	English
RARE	rare_resnet34	English
SVTR	svtr_tiny	English
CRNN_CH	crnn_resnet34_ch	Chinese
RARE_CH	rare_resnet34_ch	Chinese

The algorithm-network mapping is defined in tools/infer/text/predict_rec.py

Currently, space char recognition is not supported for the listed models. We will support it soon.

Text Detection and Recognition Concatenation

To run text spoting (i.e., detect all text regions then recognize each of them) on an input image or multiple images in a directory, please run:

python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_imgs} \
                                          --det_algorithm DB++  \
                                          --rec_algorithm CRNN

Note: set --visualize_output True if you want to visualize the detection and recognition results on the input image.

After running, the inference results will be saved in {args.draw_img_save_dir}/system_results.txt, where --draw_img_save_dir is the directory for saving results and is set to ./inference_results by default. Here are some results for examples.

Example 1:

Visualization of text detection and recognition result on img_10.jpg

, where the saved txt file is as follows

img_10.jpg	[{"transcription": "residential", "points": [[43, 88], [149, 78], [151, 101], [44, 111]]}, {"transcription": "areas", "points": [[152, 83], [201, 81], [202, 98], [153, 100]]}, {"transcription": "when", "points": [[36, 56], [101, 56], [101, 78], [36, 78]]}, {"transcription": "you", "points": [[99, 54], [143, 52], [144, 78], [100, 80]]}, {"transcription": "pass", "points": [[140, 54], [186, 50], [188, 74], [142, 78]]}, {"transcription": "by", "points": [[182, 52], [208, 52], [208, 75], [182, 75]]}, {"transcription": "volume", "points": [[199, 30], [254, 30], [254, 46], [199, 46]]}, {"transcription": "your", "points": [[164, 28], [203, 28], [203, 46], [164, 46]]}, {"transcription": "lower", "points": [[109, 25], [162, 25], [162, 46], [109, 46]]}, {"transcription": "please", "points": [[31, 18], [109, 20], [108, 48], [30, 46]]}]

Example 2:

Visualization of text detection and recognition result on web_cvpr.png

, where the saved txt file is as follows

web_cvpr.png	[{"transcription": "canada", "points": [[430, 148], [540, 148], [540, 171], [430, 171]]}, {"transcription": "vancouver", "points": [[263, 148], [420, 148], [420, 171], [263, 171]]}, {"transcription": "cvpr", "points": [[32, 69], [251, 63], [254, 174], [35, 180]]}, {"transcription": "2023", "points": [[194, 44], [256, 45], [255, 72], [194, 70]]}, {"transcription": "june", "points": [[36, 45], [110, 44], [110, 70], [37, 71]]}, {"transcription": "1822", "points": [[114, 43], [190, 45], [190, 70], [113, 69]]}]

Notes:

1. For more argument illustrations and usage, please run python tools/infer/text/predict_system.py -h or view tools/infer/text/config.py

Evaluation of the Inference Results

To infer on the whole ICDAR15 test set, please run:

python tools/infer/text/predict_system.py --image_dir /path/to/icdar15/det/test_images  /
                                          --det_algorithm {DET_ALGO}    /
                                          --rec_algorithm {REC_ALGO}  /
                                          --det_limit_type min  /
                                          --det_limit_side_len 720

Note: Here we setdet_limit_type as min for better performance, due to the input image in ICDAR15 is of high resolution (720x1280).

After running, the results including image names, bounding boxes (points) and recognized texts (transcription) will be saved in {args.draw_img_save_dir}/system_results.txt. The format of prediction results is shown as follows.

img_1.jpg	[{"transcription": "hello", "points": [600, 150, 715, 157, 714, 177, 599, 170]}, {"transcription": "world", "points": [622, 126, 695, 129, 694, 154, 621, 151]}, ...]
img_2.jpg	[{"transcription": "apple", "points": [553, 338, 706, 318, 709, 342, 556, 362]}, ...]
   ...

Prepare the ground truth file (in the same format as above), which can be obtained from the dataset conversion script in tools/dataset_converters, and run the following command to evaluate the prediction results.

python deploy/eval_utils/eval_pipeline.py --gt_path path/to/gt.txt --pred_path path/to/system_results.txt

Evaluation of the text spotting inference results on Ascend 910 with MindSpore 2.0rc1 are shown as follows.

Det. Algorithm	Rec. Algorithm	Dataset	Accuracy(%)	FPS (imgs/s)
DBNet	CRNN	ICDAR15	57.82	4.86
PSENet	CRNN	ICDAR15	47.91	1.65
PSENet (det_limit_side_len=1472 )	CRNN	ICDAR15	55.51	0.44
DBNet++	RARE	ICDAR15	59.17	3.47
DBNet++	SVTR	ICDAR15	64.42	2.49

Notes:

1. Currently, online inference pipeline is not optimized for efficiency, thus FPS is only for comparison between models. If FPS is your highest priority, please refer to Inference on Ascend 310, which is much faster.
2. Unless extra inidication, all experiments are run with --det_limit_type="min" and --det_limit_side=720.
3. SVTR is run in mixed precision mode (amp_level=O2) since it is optimized for O2.

Argument List

All CLI argument definition can be viewed via python tools/infer/text/predict_system.py -h or reading tools/infer/text/config.py.

Developer Guide - How to Add a New Model for Inference

Preprocessing

The optimal preprocessing strategy can vary from model to model, especially for the resize setting (keep_ratio, padding, etc). We define the preprocessing pipeline for each model in tools/infer/text/preprocess.py for different tasks.

If you find the default preprocessing pipeline or hyper-params does not meet the network requirement, please extend by changing the if-else conditions or adding a new key-value pair to the optimal_hparam dict in tools/infer/text/preprocess.py, where key is the algorithm name and the value is the suitable hyper-param setting for the target network inference.

Network Inference

Supported alogirhtms and their corresponding network names (which can be checked by using the list_model() API) are defined in the algo_to_model_name dict in predict_det.py and predict_rec.py.

To add a new detection model for inference, please add a new key-value pair to algo_to_model_name dict, where the key is an algorithm name and the value is the corresponding network name registered in mindocr/models/{your_model}.py.

By default, model weights will be loaded from the pro-defined URL in mindocr/models/{your_model}.py. If you want to load a local checkpoint instead, please set --det_model_dir or --rec_model_dir to the path of your local checkpoint or the directory containing a model checkpoint.

Postproprocess

Similar to preprocessing, the postprocessing method for each algorithm can vary. The postprocessing method for each algorithm is defined in tools/infer/text/postprocess.py.

If you find the default postprocessing method or hyper-params does not meet the model need, please extend the if-else conditions or add a new key-value pair to the optimal_hparam dict in tools/infer/text/postprocess.py, where the key is an algorithm name and the value is the hyper-param setting.