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¶

To be consistent with training environment.

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

Supported Detection Algorithms and Networks¶

Algorithm Name	Network Name	Language
YOLOv8	yolov8	English

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

Text direction classification¶

If there are non-upright text characters in the image, they can be classified and corrected for orientation using a text direction classifier after the detection. If you run text direction classification and correction on an input image, please perform

python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_imgs} \
                                          --det_algorithm DB++  \
                                          --rec_algorithm CRNN  \
                                          --cls_algorithm M3

The default parameter --cls_alorithm is None, which means that text direction classification is not performed. By setting --cls_alorithm, text direction classification is performed in the text detection and recognition flow. In the process of execution, the text direction classifier classifies the list of images detected by the text and corrects the direction of the non-upright images. Here are some examples of the results.

Text direction classification

word_01.png

word_02.png

Classification Results:：

word_01.png   0     1.0
word_02.png   180   1.0

The currently supported text direction classification network is mobilnet_v3, which can be set by configuring --cls_algorithm for M3. And through --cls_amp_level and --cls_model_dir to set the text direction classifier automatic mixing precision and weight file. At present, the default weight file has been configured, the default mixing precision of the network is O0, and the direction classification supports 0 and 180 degrees under the default configuration. We will support the classification of other directions in the future.

Algorithm Name	Network Name	Language
M3	mobilenet_v3	CH/EN

In addition, by setting --save_cls_result to True, text orientation classification results can be saved to {args.crop_res_save_dir}/cls_results.txt, Where --crop_res_save_dir is the directory where the results are saved.

For more parameter descriptions and usage information, please refer to tools/infer/text/config.py.

Table Structure Recognition¶

To run table structure recognition on an input image or multiple images in a directory, please run:

python tools/infer/text/predict_table_structure.py --image_dir {path_to_img or dir_to_imgs} --table_algorithm TABLE_MASTER

After running, the inference results will be saved in {args.draw_img_save_dir}, 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：

The sample image is configs/table/example.png. The inference result is as follows:

example_structure.png

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

Supported Table Structure Recognition Algorithms and Networks¶

Model Name	Backbone	Language
table_master	table_resnet_extra	universal

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

Table Structure Recognition and Text Detection Recognition Concatenation¶

To run table recognition on an input image or multiple images in a directory (i.e., recognize the table structure first, then combine the results of text detection and recognition to recognize the complete table content), and recovery to CSV files, please run:

python tools/infer/text/predict_table_recognition.py --image_dir {path_to_img or dir_to_imgs} \
                                          --det_algorithm DB_PPOCRv3  \
                                          --rec_algorithm SVTR_PPOCRv3_CH \
                                          --table_algorithm TABLE_MASTER

After running, the inference results will be saved in {args.draw_img_save_dir}, 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：

The sample image is configs/table/example.png. After online inference, the content of the CSV file is as follows:

Parameter,Non-smokers Mean± SD or N (3),Smokers Mean ± SD or N (C)
N,24,
Age (y),69.1 ± 7.0,61.5 ± 9.3 +
Males/Females,24/0,11/0
Race White/Black,19/5,9/2
Weight (kg),97.8 ± 16.8,102.5 ± 23.4
BMII (kg/m*),32.6 ± 4.9,32.6 ± 6.6
Serum albumin (g/dL),3.8 ± 0.33,3.63 ± 0.30
Serum Creatinine (mg/dL),2.75 ± 1.21,1.80 ± 0.74 *
BUN (mg/dL),46.5 ± 25.6,38.3 ± 21.8
Hemoglobin (g/dL),13.3 ± 1.6,13.5 ± 2.4
24 hour urine protein (g/d),3393 ± 2522,4423 ± 4385
lathae)mm,28.9 ± 13.8,47.2 ± 34.8 *
Duration of diabetes (yr),15.7 ± 9.1,13.3 ± 9.0
Insulin use,15 (63%),6 (55%)
"Hemoglobin A, C (%)",7.57 ± 2.02,8.98 ± 2.93
Waist/Hip Ratio,1.00 ± 0.07,1.04 ± 0.07
Antihypertensive medications,4.3 ± 1.6,3.9 ± 1.9
A,21 (88%),8 (73%)
Total Cholesterol (mg/dL),184 ± 51,223 ± 87
LDL Cholesterol (mg/dL),100 ± 44,116 ± 24
HDL Cholesterol (mg/dL),42 ± 11.1,46 ± 11.4
,17 (71%),7 (64%)

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

Layout Analysis¶

To run layout analysis on an input image or a directory containing multiple images, please execute

python tools/infer/text/predict_layout.py  --image_dir {path_to_img or dir_to_imgs} --layout_algorithm YOLOv8 --visualize_output True

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 layout analysis result on PMC4958442_00003.jpg

, where the saved layout_result.txt file is as follows

{"image_id": 0, "category_id": 1, "bbox": [308.649, 559.189, 240.211, 81.412], "score": 0.98431}
{"image_id": 0, "category_id": 1, "bbox": [50.435, 673.018, 240.232, 70.262], "score": 0.98414}
{"image_id": 0, "category_id": 3, "bbox": [322.805, 348.831, 225.949, 203.302], "score": 0.98019}
{"image_id": 0, "category_id": 1, "bbox": [308.658, 638.657, 240.31, 70.583], "score": 0.97986}
{"image_id": 0, "category_id": 1, "bbox": [50.616, 604.736, 240.044, 70.086], "score": 0.9797}
{"image_id": 0, "category_id": 1, "bbox": [50.409, 423.237, 240.132, 183.652], "score": 0.97805}
{"image_id": 0, "category_id": 1, "bbox": [308.66, 293.918, 240.181, 47.497], "score": 0.97471}
{"image_id": 0, "category_id": 1, "bbox": [308.64, 707.13, 240.271, 36.028], "score": 0.97427}
{"image_id": 0, "category_id": 1, "bbox": [308.697, 230.568, 240.062, 43.545], "score": 0.96921}
{"image_id": 0, "category_id": 4, "bbox": [51.787, 100.444, 240.267, 273.653], "score": 0.96839}
{"image_id": 0, "category_id": 5, "bbox": [308.637, 74.439, 237.878, 149.174], "score": 0.96707}
{"image_id": 0, "category_id": 1, "bbox": [50.615, 70.667, 240.068, 22.0], "score": 0.94156}
{"image_id": 0, "category_id": 2, "bbox": [50.549, 403.5, 67.392, 12.85], "score": 0.92577}
{"image_id": 0, "category_id": 1, "bbox": [51.384, 374.84, 171.939, 10.736], "score": 0.76692}

In this file, image_id is the image ID, bbox is the detected bounding box [x-coordinate of the top-left corner, y-coordinate of the bottom-right corner, width, height], score is the detection confidence, and category_id has the following meanings: - 1: text - 2: title - 3: list - 4: table - 5: figure

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

End-to-end Document Analysis and Recovery¶

To run end-to-end document analysis and recovery on an input image or multiple images in a directory (detecting all the text, table, and figure regions, recognizing words in these regions, and putting everything into docx files according to the original layout), please run:

python tools/infer/text/predict_table_e2e.py --image_dir {path_to_img or dir_to_imgs} \
                                             --det_algorithm {DET_ALGO} \
                                             --rec_algorithm {REC_ALGO}

Note: To visualize the outputs of layout, table and ocr, please set --visualize_output True.

After running, the inference results will be saved in {args.draw_img_save_dir}/{img_name}_e2e_result.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:

PMC4958442_00003.jpg Converting into docx

, where the saved txt file is as follows

{"type": "text", "bbox": [50.615, 70.667, 290.683, 92.667], "res": "tabley predictive value ofbasic clinical laboratory and suciode variables surney anc yea after tramphenins", "layout": "double"}
{"type": "table", "bbox": [51.787, 100.444, 292.054, 374.09700000000004], "res": "<html><body><table><thead><tr><td><b>sign factor</b></td><td><b>prediction valucofthe the</b></td><td><b>from difereness significance levelaf the</b></td></tr></thead><tbody><tr><td>gender</td><td>0027 0021</td><td>o442</td></tr><tr><td></td><td>00z44</td><td>0480</td></tr><tr><td>cause</td><td>tooza 0017</td><td>o547</td></tr><tr><td>cadaverieilizing donorst</td><td>0013 aont</td><td>0740</td></tr><tr><td>induction transplantation before dialysis</td><td>doattoos</td><td>0125</td></tr><tr><td>depleting antibodies monoclomalor cn immunosuppression with</td><td>doista09</td><td>0230</td></tr><tr><td>ititis</td><td>0029</td><td>aaso</td></tr><tr><td>status itional</td><td>0047 toots</td><td></td></tr><tr><td>townfrillage</td><td>non</td><td></td></tr><tr><td>transplantations number</td><td>toos 0017</td><td>o5s1</td></tr><tr><td>creatinine</td><td>02400g</td><td>caoor</td></tr><tr><td>pressure bload systolic</td><td>aidaloloss</td><td>aoz</td></tr><tr><td>pressure diastolic blood</td><td>dobetods</td><td>ass</td></tr><tr><td>hemoglobin</td><td>0044 0255t</td><td>caoor</td></tr><tr><td></td><td>004</td><td>caoor</td></tr></tbody></table></body></html>", "layout": "double"}
{"type": "text", "bbox": [51.384, 374.84, 223.32299999999998, 385.57599999999996], "res": "nanc rmeans more significant forecasting factor sign", "layout": "double"}
{"type": "title", "bbox": [50.549, 403.5, 117.941, 416.35], "res": "discussion", "layout": "double"}
{"type": "text", "bbox": [50.409, 423.237, 290.541, 606.889], "res": "determination of creatinine and hemoglobin level in the blood well aetho concentration of protein in the urine in one year atter kidney transplantation with the calculation of prognostic criterion predics the loss of renal allotransplant function in years fafter surgery advantages ff the method are the possibility oof quantitative forecasting of renal allotransplant losser which based not only its excretory function assessment but also on assessment other characteristics that may have important prognostic value and does not always directly correlate with changes in its excretors function in order the riskof death with transplant sfunctioning returntothe program hemodialysis the predictive model was implemented cabular processor excel forthe useofthe model litisquite enough the value ethel given indices calculation and prognosis will be automatically done in the electronic table figure 31", "layout": "double"}
{"type": "text", "bbox": [50.616, 604.736, 290.66, 674.822], "res": "the calculator designed by us has been patented chttpell napatentscomy 68339 sposib prognozuvannys vtrati funk caniskovogo transplanatchti and disnvailable on the in ternet chitpsolivad skillwond the accuract ot prediction of renal transplant function loss three years after transplantation was 92x", "layout": "double"}
{"type": "text", "bbox": [50.435, 673.018, 290.66700000000003, 743.28], "res": "progression of chronic renal dysfunctional the transplant accompanied the simultaneous losa the benefits of successful transplantation and the growth of problems due to immunosuppresson bosed on retrospective analysis nt resultsof treatment tofkidney transplantof the recipients with blood creatinine higher than d3 immold we adhere to the", "layout": "double"}
{"type": "figure", "bbox": [308.637, 74.439, 546.515, 223.613], "res": "./inference_results/example_figure_10.png", "layout": "double"}
{"type": "text", "bbox": [308.697, 230.568, 548.759, 274.113], "res": "figures the cnerhecadfmuthrnatical modeltor prognostication ofkidaey transplant function during the periodal three years after thetransplantation according oletectercipiolgaps after theoperation", "layout": "double"}
{"type": "text", "bbox": [308.66, 293.918, 548.841, 341.415], "res": "following principles in thecorrectionod immunisuppresion which allow decreasing the rateofs chronic dysfunctionof the transplant development or edecreasing the risk fof compliea tions incaeoflasof function", "layout": "double"}
{"type": "list", "bbox": [322.805, 348.831, 548.754, 552.133], "res": "wdo not prescribe hish doses steroids and do have the steroid pulse therapy cy do not increase the dose of received cyclosporine tacrolimus and stop medication ifthere isan increase in nephropathy tj continue immunosuppression with medicines ofmy cophenolic acid which are not nephrotoxic k4 enhance amonitoring of immunosuppression andpe vention infectious com cancel immunosuppression atreturning hemodi alysis treatment cancellation of steroids should done egradually sometimes for several months when thediscomfort eassociated transplant tempera ture main in the projection the transplanted kidney and hematurial short course of low doses of steroids administered orally of intravenously can be effective", "layout": "double"}
{"type": "text", "bbox": [308.649, 559.189, 548.86, 640.601], "res": "according to plasma concentration of creatinine the return hemodialvsis the patients were divided into groups ln the first group the creatinine concentration in blood plasma waso mmoly in the 2nd groun con centration in blood plasma was azlommaty and in the third group concentration in blood plasma was more than commolt", "layout": "double"}
{"type": "text", "bbox": [308.658, 638.657, 548.9680000000001, 709.24], "res": "dates or the return of transplant recipients with delaved rena transplant disfunction are largely dependent ion the psychological state ofthe patient severity of depression the desire to ensure the irreversibility the transplanted kidney dysfunction and fear that the dialysis will contribute to the deterioration of renal transplant function", "layout": "double"}
{"type": "text", "bbox": [308.64, 707.13, 548.911, 743.158], "res": "the survival rateof patients ofthe first group after return in hemodialysis was years and in the second and third groups respectively 53132 and28426 years", "layout": "double"}

In this file, type is the classification of the detected region, bbox is the detected bounding box [x-coordinate of the top-left corner, y-coordinate of the bottom-right corner, width, height], and res is the detected result.

Notes: 1. For more argument illustrations and usage, please run python tools/infer/text/predict_table_e2e.py -h or view tools/infer/text/config.py 2. Besides the parameters in the config.py, predict_table_e2e.py also accepts the following parameters:

Parameter	Description	Default
layout	Layout Analysis	True
ocr	Text Recognition	True
table	Table Analysis	True
recovery	Docx Convertion	True

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.