Sdcb.PaddleInference
Sdcb.PaddleOCR
Sdcb.PaddleOCR.Models.Local
Sdcb.PaddleInference.runtime.win64.mkl
OpenCvSharp4.runtime.win

Windows(Online model): Detection and Recognition(All)

1. Install NuGet Packages:

   Sdcb.PaddleInference
   Sdcb.PaddleOCR
   Sdcb.PaddleOCR.Models.Online
   Sdcb.PaddleInference.runtime.win64.mkl
   OpenCvSharp4.runtime.win
   

2. Using following C# code to get result:

   FullOcrModel model = await OnlineFullModels.EnglishV3.DownloadAsync();
   
   byte[] sampleImageData;
   string sampleImageUrl = @"https://www.tp-link.com.cn/content/images2017/gallery/4288_1920.jpg";
   using (HttpClient http = new HttpClient())
   {
       Console.WriteLine("Download sample image from: " + sampleImageUrl);
       sampleImageData = await http.GetByteArrayAsync(sampleImageUrl);
   }
   
   using (PaddleOcrAll all = new PaddleOcrAll(model, PaddleDevice.Mkldnn())
   {
       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 22.04): Detection and Recognition(All)

1. Use sdflysha/sdflysha/dotnet6-paddle:2.5.0-ubuntu22 to replace mcr.microsoft.com/dotnet/aspnet:6.0 in Dockerfile as docker base image.

The build steps for sdflysha/dotnet6-paddle:2.5.0-ubuntu22 was described here.

2. Install NuGet Packages:

dotnet add package Sdcb.PaddleOCR.Models.Local

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.

3. write following C# code to get result(also can be exactly the same as windows):

FullOcrModel model = LocalFullModels.ChineseV3;
using (PaddleOcrAll all = new PaddleOcrAll(model, PaddleDevice.Mkldnn()))
// 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.PaddleOCR
// Sdcb.PaddleOCR.Models.Local
// Sdcb.PaddleInference.runtime.win64.mkl (required in Windows, linux using docker)
// OpenCvSharp4.runtime.win (required in Windows, linux using docker)
byte[] sampleImageData;
string sampleImageUrl = @"https://www.tp-link.com.cn/content/images2017/gallery/4288_1920.jpg";
using (HttpClient http = new HttpClient())
{
    Console.WriteLine("Download sample image from: " + sampleImageUrl);
    sampleImageData = await http.GetByteArrayAsync(sampleImageUrl);
}

using (PaddleOcrDetector detector = new PaddleOcrDetector(LocalDetectionModel.ChineseV3, PaddleDevice.Mkldnn()))
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);
    }
}

Table recognition

// Install following packages:
// Sdcb.PaddleInference
// Sdcb.PaddleOCR
// Sdcb.PaddleOCR.Models.Local
// Sdcb.PaddleInference.runtime.win64.mkl (required in Windows, linux using docker)
// OpenCvSharp4.runtime.win (required in Windows, linux using docker)
using PaddleOcrTableRecognizer tableRec = new(LocalTableRecognitionModel.ChineseMobileV2_SLANET);
using Mat src = Cv2.ImRead(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.MyPictures), "table.jpg"));
// Table detection
TableDetectionResult tableResult = tableRec.Run(src);

// Normal OCR
using PaddleOcrAll all = new(LocalFullModels.ChineseV3);
all.Detector.UnclipRatio = 1.2f;
PaddleOcrResult ocrResult = all.Run(src);

// Rebuild table
string html = tableResult.RebuildTable(ocrResult);

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

PaddleConfig.MkldnnCacheCapacity

Default value: 1

This value has a positive correlation to the peak of memory usage that used by mkldnn and a negative correlation to the performance when providing different images.

To figure out each value corresponding to the peak memory usage, you should run the detection for various images(using the same image will not increase memory usage) continuously till the memory usage get stable within a variation of 1GB.

For more details please check the pr #46 that decreases the default value and the Paddle document for MkldnnCacheCapacity.

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: 1536

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.

How can I improve performance?

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

FAQ

How to integrate Sdcb.PaddleOCR to ASP.NET Core?

Please refer to this demo website, it contains a tutorial: https://github.com/sdcb/paddlesharp-ocr-aspnetcore-demo

In your service builder code, register a QueuedPaddleOcrAll Singleton:

builder.Services.AddSingleton(s =>
{
    Action<PaddleConfig> device = builder.Configuration["PaddleDevice"] == "GPU" ? PaddleDevice.Gpu() : PaddleDevice.Mkldnn();
    return new QueuedPaddleOcrAll(() => new PaddleOcrAll(LocalFullModels.ChineseV3, device)
    {
        Enable180Classification = true,
        AllowRotateDetection = true,
    }, consumerCount: 1);
});

In your controller, use the registered QueuedPaddleOcrAll singleton:

public class OcrController : Controller
{
    private readonly QueuedPaddleOcrAll _ocr;

    public OcrController(QueuedPaddleOcrAll ocr) { _ocr = ocr; }

    [Route("ocr")]
    public async Task<OcrResponse> Ocr(IFormFile file)
    {
        using MemoryStream ms = new();
        using Stream stream = file.OpenReadStream();
        stream.CopyTo(ms);
        using Mat src = Cv2.ImDecode(ms.ToArray(), ImreadModes.Color);
        double scale = 1;
        using Mat scaled = src.Resize(default, scale, scale);

        Stopwatch sw = Stopwatch.StartNew();
        string textResult = (await _ocr.Run(scaled)).Text;
        sw.Stop();

        return new OcrResponse(textResult, sw.ElapsedMilliseconds);
    }
}

How to migrate previous old version to latest 2.6.0.1?

• Remove PaddleConfig.Default.* settings because it's delted in 2.6.0.1

• Add one of following config in 2nd parameter in PaddleOcrAll:

  • PaddleDevice.Openblas()
  • PaddleDevice.Mkldnn()
  • PaddleDevice.Onnx()
  • PaddleDevice.Gpu()
  • PaddleDevice.Gpu().And(PaddleDevice.TensorRt(...))

  

How to migrate < 2.7.0 version to latest 2.7.0 when using LocalV3?

• Uninstall NuGet package: Sdcb.PaddleOCR.Models.LocalV3
• Install NuGet pakcage: Sdcb.PaddleOCR.Models.Local
• Update namespaces from Sdcb.PaddleOCR.Models.LocalV3 to Sdcb.PaddleOCR.Models.Local

TensorRT 🚄

To use TensorRT, just specify PaddleDevice.Gpu().And(PaddleDevice.TensorRt("shape-info.txt")) instead of PaddleDevice.Gpu() to make it work. 💡

Please be aware, this shape info text file **.txt is bound to your model. Different models have different shape info, so if you're using a complex model like Sdcb.PaddleOCR, you should use different shapes for different models like this:

using PaddleOcrAll all = new(model,
   PaddleDevice.Gpu().And(PaddleDevice.TensorRt("det.txt")),
   PaddleDevice.Gpu().And(PaddleDevice.TensorRt("cls.txt")),
   PaddleDevice.Gpu().And(PaddleDevice.TensorRt("rec.txt")))
{
   Enable180Classification = true,
   AllowRotateDetection = true,
};

In this case:

• DetectionModel will use det.txt 🔍
• 180DegreeClassificationModel will use cls.txt 🔃
• RecognitionModel will use rec.txt 🔡

NOTE 📝:

The first round of TensorRT running will generate a shape info **.txt file in this folder: %AppData%\Sdcb.PaddleInference\TensorRtCache. It will take around 100 seconds to finish TensorRT cache generation. After that, it should be faster than the general GPU. 🚀

In this case, if something strange happens (for example, you mistakenly create the same shape-info.txt file for different models), you can delete this folder to generate TensorRT cache again: %AppData%\Sdcb.PaddleInference\TensorRtCache. 🗑️