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MindOCR Offline Inference

Introduction

MindOCR inference supports Ascend310/Ascend310P devices, supports MindSpore Lite inference backend, integrates text detection, angle classification, and text recognition, implements end-to-end OCR inference process, and optimizes inference performance using pipeline parallelism.

MindOCR supported models can find in MindOCR models list，PPOCR models list, You can jump to the models list page to download MindIR/ONNX for converting MindSpore Lite offline models.

The overall process of MindOCR Lite inference is as follows:

graph LR;
    A[MindOCR models] -- export --> B[MindIR] -- converter_lite --> C[MindSpore Lite MindIR];
    D[ThirdParty models] -- xx2onnx --> E[ONNX] -- converter_lite --> C;
    C --input --> F[MindOCR Infer] -- outputs --> G[Evaluation];
    H[images] --input --> F[MindOCR Infer];

2. Environment Instalation

Please refer to Offline Inference Environment Installation.

3. Model conversion

Please refer to Model Converter Tutorial.

4. Inference (Python)

Enter the inference directory：cd deploy/py_infer.

4.1 Detection + Classification + Recognition

python infer.py \
    --input_images_dir=/path/to/images \
    --det_model_path=/path/to/mindir/dbnet_resnet50.mindir \
    --det_model_name_or_config=../../configs/det/dbnet/db_r50_icdar15.yaml \
    --cls_model_path=/path/to/mindir/cls_mv3.mindir \
    --cls_model_name_or_config=ch_pp_mobile_cls_v2.0 \
    --rec_model_path=/path/to/mindir/crnn_resnet34.mindir \
    --rec_model_name_or_config=../../configs/rec/crnn/crnn_resnet34.yaml \
    --res_save_dir=det_cls_rec \
    --vis_pipeline_save_dir=det_cls_rec

Note: set --character_dict_path=/path/to/xxx_dict.txt if not only use numbers and lowercase.

The visualization images are stored in det_cls_rec, as shown in the picture.

Visualization of text detection and recognition result

The results are saved in det_cls_rec/pipeline_results.txt in the following format:

img_182.jpg [{"transcription": "cocoa", "points": [[14.0, 284.0], [222.0, 274.0], [225.0, 325.0], [17.0, 335.0]]}, {...}]

4.2 Detection + Recognition

If you don't enter the parameters related to classification, it will skip and only perform detection+recognition.

python infer.py \
    --input_images_dir=/path/to/images \
    --det_model_path=/path/to/mindir/dbnet_resnet50.mindir \
    --det_model_name_or_config=../../configs/det/dbnet/db_r50_icdar15.yaml \
    --rec_model_path=/path/to/mindir/crnn_resnet34.mindir \
    --rec_model_name_or_config=../../configs/rec/crnn/crnn_resnet34.yaml \
    --res_save_dir=det_rec \
    --vis_pipeline_save_dir=det_rec

Note: set --character_dict_path=/path/to/xxx_dict.txt if not only use numbers and lowercase.

The visualization images are stored in det_rec folder, as shown in the picture.

Visualization of text detection and recognition result

The recognition results are saved in det_rec/pipeline_results.txt in the following format:

img_498.jpg [{"transcription": "keep", "points": [[819.0, 71.0], [888.0, 67.0], [891.0, 104.0], [822.0, 108.0]]}, {...}]

4.3 Detection

Run text detection alone.

python infer.py \
    --input_images_dir=/path/to/images \
    --det_model_path=/path/to/mindir/dbnet_resnet50.mindir \
    --det_model_name_or_config=../../configs/det/dbnet/db_r50_icdar15.yaml \
    --res_save_dir=det \
    --vis_det_save_dir=det

The visualization results are stored in the det folder, as shown in the picture.

Visualization of text detection result

The detection results are saved in the det/det_results.txt file in the following format:

img_108.jpg [[[226.0, 442.0], [402.0, 416.0], [404.0, 433.0], [228.0, 459.0]], [...]]

4.4 Classification

Run text angle classification alone.

# cls_mv3.mindir is converted from ppocr
python infer.py \
    --input_images_dir=/path/to/images \
    --cls_model_path=/path/to/mindir/cls_mv3.mindir \
    --cls_model_name_or_config=ch_pp_mobile_cls_v2.0 \
    --res_save_dir=cls

The results will be saved in cls/cls_results.txt, with the following format:

word_867.png   ["180", 0.5176]
word_1679.png  ["180", 0.6226]
word_1189.png  ["0", 0.9360]

4.5 Recognition

Run text recognition alone.

python infer.py \
    --input_images_dir=/path/to/images \
    --backend=lite \
    --rec_model_path=/path/to/mindir/crnn_resnet34.mindir \
    --rec_model_name_or_config=../../configs/rec/crnn/crnn_resnet34.yaml \
    --res_save_dir=rec

Note: set --character_dict_path=/path/to/xxx_dict.txt if not only use numbers and lowercase.

The results will be saved in rec/rec_results.txt, with the following format:

word_421.png   "under"
word_1657.png  "candy"
word_1814.png  "cathay"

4.6 Detail of inference parameter

Details - Basic settings | name | type | default | description | |:-----------------|:-----|:--------|:---------------------------------------------------------| | input_images_dir | str | None | Image or folder path for inference | | device | str | Ascend | Device type, support Ascend | | device_id | int | 0 | Device id | | backend | str | lite | Inference backend, support lite | | parallel_num | int | 1 | Number of parallel in each stage of pipeline parallelism | | precision_mode | str | None | Precision mode, only supports setting by [Model Conversion](convert_tutorial.md) currently, and it takes no effect here | - Saving Result | name | type | default | description | |:----------------------|:-----|:------------------|:-------------------------------------------------------| | res_save_dir | str | inference_results | Saving dir for inference results | | vis_det_save_dir | str | None | Saving dir for images of with detection boxes | | vis_pipeline_save_dir | str | None | Saving dir for images of with detection boxes and text | | vis_font_path | str | None | Font path for drawing text | | crop_save_dir | str | None | Saving path for cropped images after detection | | show_log | bool | False | Whether show log when inferring | | save_log_dir | str | None | Log saving dir | - Text detection | name | type | default | description | |:-------------------------|:-----|:--------|:-------------------------------------------------------| | det_model_path | str | None | Model path for text detection | | det_model_name_or_config | str | None | Model name or YAML config file path for text detection | - Text angle classification | name | type | default | description | |:-------------------------|:-----|:--------|:------------------------------------------------------------------| | cls_model_path | str | None | Model path for text angle classification | | cls_model_name_or_config | str | None | Model name or YAML config file path for text angle classification | - Text recognition | name | type | default | description | |:-------------------------|:-----|:--------|:----------------------------------------------------------------------------| | rec_model_path | str | None | Model path for text recognition | | rec_model_name_or_config | str | None | Model name or YAML config file path for text recognition | | character_dict_path | str | None | Dict file for text recognition，default only supports numbers and lowercase | Notes： `*_model_name_or_config` can be the model name or YAML config file path, please refer to [MindOCR models list](mindocr_models_list.md)，[PPOCR models list](thirdparty_models_list.md).

5. Model Inference Evaluation

5.1 Text detection

After inference, please use the following command to evaluate the results:

python deploy/eval_utils/eval_det.py \
    --gt_path=/path/to/det_gt.txt \
    --pred_path=/path/to/prediction/det_results.txt

5.2 Text recognition

After inference, please use the following command to evaluate the results:

python deploy/eval_utils/eval_rec.py \
    --gt_path=/path/to/rec_gt.txt \
    --pred_path=/path/to/prediction/rec_results.txt \
    --character_dict_path=/path/to/xxx_dict.txt

Please note that character_dict_path is an optional parameter, and the default dictionary only supports numbers and English lowercase.

When evaluating the PaddleOCR series models, please refer to Third-party Model Support List to use the corresponding dictionary.