This document gives the performance of the series models for Chinese and English recognition.
## TEST DATA
We collected 300 images for different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc. The following figure shows some images of the test set.
- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization.
- The long size of the input for the text detector is 960.
- The evaluation time-consuming stage is the complete stage from image input to result output, including image pre-processing and post-processing.
-```Intel Xeon 6148``` is the server-side CPU model. Intel MKL-DNN is used in the test to accelerate the CPU prediction speed.
-```Snapdragon 855``` is a mobile processing platform model.
Compares the model size and F-score:
| Model Name | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score |
Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models:
| Model Name | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score | SD 855<br>\(ms\) |
This document gives the prediction time-consuming benchmark of PaddleOCR Ultra Lightweight Chinese Model (8.6M) on each platform.
This document gives the performance of the series models for Chinese and English recognition.
## TEST DATA
* 500 images were randomly sampled from the Chinese public data set [ICDAR2017-RCTW](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#ICDAR2017-RCTW-17).
Most of the pictures in the set were collected in the wild through mobile phone cameras.
Some are screenshots.
These pictures show various scenes, including street scenes, posters, menus, indoor scenes and screenshots of mobile applications.
## MEASUREMENT
The predicted time-consuming indicators on the four platforms are as follows:
We collected 300 images for different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc. The following figure shows some images of the test set.
* Use parameters ```--enable_mkldnn True``` to turn on the acceleration switch when making predictions
*```Snapdragon 855``` is a mobile processing platform model.
- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization.
- The long size of the input for the text detector is 960.
- The evaluation time-consuming stage is the complete stage from image input to result output, including image pre-processing and post-processing.
-```Intel Xeon 6148``` is the server-side CPU model. Intel MKL-DNN is used in the test to accelerate the CPU prediction speed.
-```Snapdragon 855``` is a mobile processing platform model.
Compares the model size and F-score:
| Model Name | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score |
Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models:
| Model Name | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score | SD 855<br>\(ms\) |
