PaddleSharp

💗.NET Wrapper for PaddleInference C API, include PaddleOCR, support 14 OCR languages model download on-demand, support Windows(x64) and Linux(Ubuntu-20.04 x64).

NuGet Packages/Docker Images

NuGet Package	Version	Description
Sdcb.PaddleInference		Paddle Inference C API .NET binding
Sdcb.PaddleOCR		PaddleOCR library(based on Sdcb.PaddleInference)
Sdcb.PaddleOCR.KnownModels		Helper to download PaddleOCR models
Sdcb.PaddleInference.runtime.win64.mkl		Paddle Inference C API Windows x64(mkl-dnn) Native binding
Sdcb.PaddleInference.runtime.win64.cuda11_cudnn8_tr7		Paddle Inference C API Windows x64(GPU CUDA 11/cuDNN 8.0/TensorRT 7) Native binding

Note: Linux does not need a native binding NuGet package like windows(Sdcb.PaddleInference.runtime.win64.mkl), instead, you can/should based from a Dockerfile to development:

Docker Images	Version	Description
sdflysha/ubuntu20-dotnet6-paddleocr2.2.1		PaddleInference 2.2.1, OpenCV 4.5.3, based on official Ubuntu 20.04 .NET 6 Runtime
sdflysha/ubuntu20-dotnet6sdk-paddleocr2.2.1		PaddleInference 2.2.1, OpenCV 4.5.3, based on official Ubuntu 20.04 .NET 6 SDK

Usage

Windows: Detection and Recognition(All)

Install NuGet Packages:

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

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)

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.

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.

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:

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

Optimize parameters and performance hints

PaddleOcrAll.Enable180Classification

Default value: true

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

Enable GPU support can significantly improve the throughput and lower the CPU usage.

Steps to use GPU in windows:

(for windows) Install the package: Sdcb.PaddleInference.runtime.win64.cuda11_cudnn8_tr7 instead of Sdcb.PaddleInference.runtime.win64.mkl, do not install both.
Install CUDA from NVIDIA, and configure environment variables to PATH or LD_LIBRARY_PATH(linux)
Install cuDNN from NVIDIA, and configure environment variables to PATH or LD_LIBRARY_PATH(linux)
Install TensorRT from NVIDIA, and configure environment variables to PATH or LD_LIBRARY_PATH(linux)

You can refer this blog page for GPU in Windows: 关于PaddleSharp GPU使用常见问题记录

If you're using Linux, you need to compile your own OpenCvSharp4 environment following the docker build scripts follow the CUDA/cuDNN/TensorRT configuration tasks.

After these steps completed, you can try specify PaddleConfig.Defaults.UseGpu = true in begin of your code and then enjoy😁.

FAQ

Why my code runs good in my windows machine, but DllNotFoundException in other machine:

Please ensure the latest Visual C++ Redistributable was installed in Windows(typically it should automatically installed if you have Visual Studio installed) Otherwise, it will failed with following error(Windows only):

DllNotFoundException: Unable to load DLL 'paddle_inference_c' or one of its dependencies (0x8007007E)

How can I improve performance?

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

Contact

QQ group of C#/.NET computer vision technical communicate(C#/.NET计算机视觉技术交流群): 579060605

lwqwag/PaddleSharp