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Usage

Windows: Detection and Recognition(All)

  1. Install NuGet Packages:

    Sdcb.PaddleInference
Sdcb.PaddleInference.runtime.win64.mkl
Sdcb.PaddleOCR
Sdcb.PaddleOCR.KnownModels
OpenCvSharp4
OpenCvSharp4.runtime.win

  2. Using following C# code to get result:

    OCRModel model = KnownOCRModel.PPOcrV2;
await model.EnsureAll();

byte[] sampleImageData;
string sampleImageUrl = @"https://www.tp-link.com.cn/content/images/detail/2164/TL-XDR5450易展Turbo版-3840px_03.jpg";
using (HttpClient http = new HttpClient())
{
    Console.WriteLine("Download sample image from: " + sampleImageUrl);
    sampleImageData = await http.GetByteArrayAsync(sampleImageUrl);
}

using (PaddleOcrAll all = new PaddleOcrAll(model.RootDirectory, model.KeyPath)
{
    AllowRotateDetection = true, /* 允许识别有角度的文字 */ 
    Enable180Classification = false, /* 允许识别旋转角度大于90度的文字 */
})
{
    // Load local file by following code:
    // using (Mat src2 = Cv2.ImRead(@"C:\test.jpg"))
    using (Mat src = Cv2.ImDecode(sampleImageData, ImreadModes.Color))
    {
        PaddleOcrResult result = all.Run(src);
        Console.WriteLine("Detected all texts: \n" + result.Text);
        foreach (PaddleOcrResultRegion region in result.Regions)
        {
            Console.WriteLine($"Text: {region.Text}, Score: {region.Score}, RectCenter: {region.Rect.Center}, RectSize:    {region.Rect.Size}, Angle: {region.Rect.Angle}");
        }
    }
}

Linux(Ubuntu 20.04): Detection and Recognition(All)

  1. Use sdflysha/ubuntu20-dotnet6-paddleocr2.2.1:20211223 to replace mcr.microsoft.com/dotnet/aspnet:6.0 in Dockerfile as docker base image.

The build steps for ubuntu20-dotnet6-paddleocr was described here.

And also, we also provided another dotnet6-sdk Dockerfile, described here.

  1. Install NuGet Packages:
dotnet add package Sdcb.PaddleInference
dotnet add package Sdcb.PaddleOCR
dotnet add package Sdcb.PaddleOCR.KnownModels
dotnet add package OpenCvSharp4
dotnet add package OpenCvSharp4.runtime.ubuntu.18.04-x64

Please aware in Linux, the native binding library is not required, instead, you should compile your own OpenCV/PaddleInference library, or just use the Docker image.

  1. write following C# code to get result(also can be exactly the same as windows):
OCRModel model = KnownOCRModel.PPOcrV2;
await model.EnsureAll();
using (PaddleOcrAll all = new PaddleOcrAll(model.RootDirectory, model.KeyPath))
// Load in-memory data by following code:
// using (Mat src = Cv2.ImDecode(sampleImageData, ImreadModes.Color))
using (Mat src = Cv2.ImRead(@"/app/test.jpg"))
{
    Console.WriteLine(all.Run(src).Text);
}

Detection Only

// Install following packages:
// Sdcb.PaddleInference
// Sdcb.PaddleInference.runtime.win64.mkl (required in Windows)
// Sdcb.PaddleOCR
// Sdcb.PaddleOCR.KnownModels
// OpenCvSharp4
// OpenCvSharp4.runtime.win (required in Windows)
// OpenCvSharp4.runtime.linux18.04 (required in Linux)
byte[] sampleImageData;
string sampleImageUrl = @"https://www.tp-link.com.cn/content/images/detail/2164/TL-XDR5450易展Turbo版-3840px_03.jpg";
using (HttpClient http = new HttpClient())
{
    Console.WriteLine("Download sample image from: " + sampleImageUrl);
    sampleImageData = await http.GetByteArrayAsync(sampleImageUrl);
}

OCRModel model = KnownOCRModel.PPOcrV2;
await model.EnsureAll();
using (PaddleOcrDetector detector = new PaddleOcrDetector(model.DetectionDirectory))
using (Mat src = Cv2.ImDecode(sampleImageData, ImreadModes.Color))
{
    RotatedRect[] rects = detector.Run(src);
    using (Mat visualized = PaddleOcrDetector.Visualize(src, rects, Scalar.Red, thickness: 2))
    {
        string outputFile = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.MyPictures), "output.jpg");
        Console.WriteLine("OutputFile: " + outputFile);
        visualized.ImWrite(outputFile);
    }
}

Language supports

Language 中文名 Code
Chinese 简体中文 KnownOCRModel.PPOcrV2
Chinese Server 简体中文(服务器版) KnownOCRModel.PPOcrServerV2
English 英文 KnownOCRModel.EnglishMobileV2
Tranditional Chinese 繁体中文 KnownOCRModel.EnglishMobileV2
French 法文 KnownOCRModel.FrenchMobileV2
German 德文 KnownOCRModel.GermanMobileV2
Korean 韩文 KnownOCRModel.KoreanMobileV2
Japanese 日文 KnownOCRModel.JapaneseMobileV2
Telugu 泰卢固文 KnownOCRModel.TeluguMobileV2
Kannada 卡纳达文 KnownOCRModel.KannadaMobileV2
Tamil 泰米尔文 KnownOCRModel.TamilMobileV2
Latin 拉丁文 KnownOCRModel.LatinMobileV2
Arabic 阿拉伯字母 KnownOCRModel.ArabicMobileV2
Cyrillic 斯拉夫字母 KnownOCRModel.CyrillicMobileV2
Devanagari 梵文字母 KnownOCRModel.DevanagariMobileV2

Just replace the KnownOCRModel.PPOcrV2 in demo code with your speicific language in Code column above, then you can use the language.

Technical details

There is 3 steps to do OCR:

  1. Detection - Detect text's position, angle and area (PaddleOCRDetector)
  2. Classification - Determin whether text should rotate 180 degreee.
  3. Recognization - Recognize the area into text

Optimize parameters and performance hints

PaddleOcrAll.Enable180Classification

Default value: false

This directly effect the step 2, set to false can skip this step, which will unable to detect text from right to left(which should be acceptable because most text direction is from left to right).

Close this option can make the full process about ~10% faster.

PaddleOcrAll.AllowRotateDetection

Default value: true

This allows detect any rotated texts. If your subject is 0 degree text (like scaned table or screenshot), you can set this parameter to false, which will improve OCR accurancy and little bit performance.

PaddleOcrAll.Detector.MaxSize

Default value: 2048

This effect the the max size of step #1, lower this value can improve performance and reduce memory usage, but will also lower the accurancy.

You can also set this value to null, in that case, images will not scale-down to detect, performance will drop and memory will high, but should able to get better accurancy.

PaddleConfig.Defaults.UseGpu

Default value: false

If you wants to use GPU, you should refer to FAQ How to enable GPU? section, CUDA/cuDNN/TensorRT need to be installed manually.

How can I improve performance?

Please review the Technical details section and read the Optimize parameters and performance hints section, or UseGpu.