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# BENCHMARK
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This document gives the prediction time-consuming benchmark of PaddleOCR Ultra Lightweight Chinese Model (8.6M) on each platform.
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## TEST DATA
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* 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).
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Most of the pictures in the set were collected in the wild through mobile phone cameras.
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Some are screenshots.
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These pictures show various scenes, including street scenes, posters, menus, indoor scenes and screenshots of mobile applications.
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## MEASUREMENT
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The predicted time-consuming indicators on the four platforms are as follows:
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| Long size(px) | T4(s) | V100(s) | Intel Xeon 6148(s) | Snapdragon 855(s) |
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| :---------: | :-----: | :-------: | :------------------: | :-----------------: |
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| 960 | 0.092 | 0.057 | 0.319 | 0.354 |
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| 640 | 0.067 | 0.045 | 0.198 | 0.236 |
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| 480 | 0.057 | 0.043 | 0.151 | 0.175 |
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Explanation:
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* The evaluation time-consuming stage is the complete stage from image input to result output, including image
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pre-processing and post-processing.
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* ```Intel Xeon 6148``` is the server-side CPU model. Intel MKL-DNN is used in the test to accelerate the CPU prediction speed.
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To use this operation, you need to:
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* Update to the latest version of PaddlePaddle: https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev
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Please select the corresponding mkl version wheel package according to the CUDA version and Python version of your environment,
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for example, CUDA10, Python3.7 environment, you should:
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```
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# Obtain the installation package
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wget https://paddle-wheel.bj.bcebos.com/0.0.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-0.0.0-cp37-cp37m-linux_x86_64.whl
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# Installation
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pip3.7 install paddlepaddle_gpu-0.0.0-cp37-cp37m-linux_x86_64.whl
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```
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* Use parameters ```--enable_mkldnn True``` to turn on the acceleration switch when making predictions
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* ```Snapdragon 855``` is a mobile processing platform model.
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# DATA ANNOTATION TOOLS
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There are the commonly used data annotation tools, which will be continuously updated. Welcome to contribute tools~
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1.**labelImg**
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* Tool description: Rectangular label
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* Tool address: https://github.com/tzutalin/labelImg
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* Sketch diagram:
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2.**roLabelImg**
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* Tool description: Label tool rewritten based on labelImg, supporting rotating rectangular label
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* Tool address: https://github.com/cgvict/roLabelImg
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* Sketch diagram:
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3.**labelme**
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* Tool description: Support four points, polygons, circles and other labels
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* Tool address: https://github.com/wkentaro/labelme
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* Sketch diagram:
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# DATA SYNTHESIS TOOLS
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In addition to open source data, users can also use synthesis tools to synthesize data.
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There are the commonly used data synthesis tools, which will be continuously updated. Welcome to contribute tools~
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* [Text_renderer](https://github.com/Sanster/text_renderer)
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* [SynthText](https://github.com/ankush-me/SynthText)
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* [SynthText_Chinese_version](https://github.com/JarveeLee/SynthText_Chinese_version)
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* [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator)
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* [SynthText3D](https://github.com/MhLiao/SynthText3D)
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* [UnrealText](https://github.com/Jyouhou/UnrealText/)
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# Handwritten OCR dataset
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Here we have sorted out the commonly used handwritten OCR dataset datasets, which are being updated continuously. We welcome you to contribute datasets ~
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- [Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset](#Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset)
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- [NIST handwritten single character dataset - English](#NIST handwritten single character dataset - English)
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<a name="Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset"></a>
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## Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset
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- **Data source**:http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
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- **Data introduction**:
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* It includes online and offline handwritten data,`HWDB1.0~1.2` has totally 3895135 handwritten single character samples, which belong to 7356 categories (7185 Chinese characters and 171 English letters, numbers and symbols);`HWDB2.0~2.2` has totally 5091 pages of images, which are divided into 52230 text lines and 1349414 words. All text and text samples are stored as grayscale images. Some sample words are shown below.
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- **Download address**:http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
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- **使用建议**:Data for single character, white background, can form a large number of text lines for training. White background can be processed into transparent state, which is convenient to add various backgrounds. For the case of semantic needs, it is suggested to extract single character from real corpus to form text lines.
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<a name="NIST handwritten single character dataset - English"></a>
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## NIST handwritten single character dataset - English(NIST Handprinted Forms and Characters Database)
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- **Data source**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
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- **Data introduction**: NIST19 dataset is suitable for handwritten document and character recognition model training. It is extracted from the handwritten sample form of 3600 authors and contains 810000 character images in total. Nine of them are shown below.
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- **Download address**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
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