Sdcb.PaddleOCR
2.6.0.6-preview.3
Prefix Reserved
See the version list below for details.
dotnet add package Sdcb.PaddleOCR --version 2.6.0.6-preview.3
NuGet\Install-Package Sdcb.PaddleOCR -Version 2.6.0.6-preview.3
<PackageReference Include="Sdcb.PaddleOCR" Version="2.6.0.6-preview.3" />
paket add Sdcb.PaddleOCR --version 2.6.0.6-preview.3
#r "nuget: Sdcb.PaddleOCR, 2.6.0.6-preview.3"
// Install Sdcb.PaddleOCR as a Cake Addin #addin nuget:?package=Sdcb.PaddleOCR&version=2.6.0.6-preview.3&prerelease // Install Sdcb.PaddleOCR as a Cake Tool #tool nuget:?package=Sdcb.PaddleOCR&version=2.6.0.6-preview.3&prerelease
Language supports
Please refer to https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_en/models_list_en.md to check language support models.
Just replace the .ChineseV3
in demo code with your speicific language, then you can use the language.
Usage
Windows(Local model): Detection and Recognition(All)
Install NuGet Packages:
Sdcb.PaddleInference Sdcb.PaddleOCR Sdcb.PaddleOCR.Models.LocalV3 Sdcb.PaddleInference.runtime.win64.mkl OpenCvSharp4.runtime.win
Using following C# code to get result:
FullOcrModel model = LocalFullModels.ChineseV3; 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($"Text: {region.Text}, Score: {region.Score}, RectCenter: {region.Rect.Center}, RectSize: {region.Rect.Size}, Angle: {region.Rect.Angle}"); } } }
Windows(Online model): Detection and Recognition(All)
Install NuGet Packages:
Sdcb.PaddleInference Sdcb.PaddleOCR Sdcb.PaddleOCR.Models.Online Sdcb.PaddleInference.runtime.win64.mkl OpenCvSharp4.runtime.win
Using following C# code to get result:
FullOcrModel model = await OnlineFullModels.EnglishV3.DownloadAsync(); byte[] sampleImageData; string sampleImageUrl = @"https://www.tp-link.com.cn/content/images2017/gallery/4288_1920.jpg"; using (HttpClient http = new HttpClient()) { Console.WriteLine("Download sample image from: " + sampleImageUrl); sampleImageData = await http.GetByteArrayAsync(sampleImageUrl); } using (PaddleOcrAll all = new PaddleOcrAll(model, PaddleDevice.Mkldnn()) { AllowRotateDetection = true, /* 允许识别有角度的文字 */ Enable180Classification = false, /* 允许识别旋转角度大于90度的文字 */ }) { // Load local file by following code: // using (Mat src2 = Cv2.ImRead(@"C:\test.jpg")) using (Mat src = Cv2.ImDecode(sampleImageData, ImreadModes.Color)) { PaddleOcrResult result = all.Run(src); Console.WriteLine("Detected all texts: \n" + result.Text); foreach (PaddleOcrResultRegion region in result.Regions) { Console.WriteLine($"Text: {region.Text}, Score: {region.Score}, RectCenter: {region.Rect.Center}, RectSize: {region.Rect.Size}, Angle: {region.Rect.Angle}"); } } }
Linux(Ubuntu 20.04): Detection and Recognition(All)
- Use
sdflysha/sdflysha/dotnet6-paddle:2.3.0-ubuntu20
to replacemcr.microsoft.com/dotnet/aspnet:6.0
inDockerfile
as docker base image.
The build steps for sdflysha/dotnet6-paddle:2.3.0-ubuntu20
was described here.
- Install NuGet Packages:
dotnet add package Sdcb.PaddleOCR.Models.LocalV3
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):
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.LocalV3
// 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.LocalV3
// 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);
Raw table | Table model output | Rebuilt table |
---|---|---|
![]() |
![]() |
![]() |
Paddle Devices
Mkldnn -
PaddleDevice.Mkldnn()
Generally fast
Openblas -
PaddleDevice.Openblas()
Much slower, but binary file smaller and consume lesser memory
Gpu -
PaddleDevice.Gpu()
Much faster but relies on NVIDIA GPU and CUDA
If you wants to use GPU, you should refer to FAQ
How to enable GPU?
section, CUDA/cuDNN/TensorRT need to be installed manually.TensorRT -
PaddleDevice.Gpu().And(PaddleDevice.TensorRt("shape-info.txt"))
Even faster than raw Gpu but need install TensorRT environment.
Please refer to this page for more details
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
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(Size.Zero, 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?
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
.NET Core | netcoreapp2.0 was computed. netcoreapp2.1 was computed. netcoreapp2.2 was computed. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
.NET Standard | netstandard2.0 is compatible. netstandard2.1 was computed. |
.NET Framework | net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
MonoAndroid | monoandroid was computed. |
MonoMac | monomac was computed. |
MonoTouch | monotouch was computed. |
Tizen | tizen40 was computed. tizen60 was computed. |
Xamarin.iOS | xamarinios was computed. |
Xamarin.Mac | xamarinmac was computed. |
Xamarin.TVOS | xamarintvos was computed. |
Xamarin.WatchOS | xamarinwatchos was computed. |
-
.NETStandard 2.0
- OpenCvSharp4 (>= 4.7.0.20230115)
- Sdcb.PaddleInference (>= 2.5.0-preview.3)
NuGet packages (10)
Showing the top 5 NuGet packages that depend on Sdcb.PaddleOCR:
Package | Downloads |
---|---|
Sdcb.PaddleOCR.Models.Local
Known models for PaddleOCR |
|
Sdcb.PaddleOCR.Models.Online
Provides on-demand downloading of PaddleOCR models. |
|
Wlkr.SafePaddleOCR
基于PaddleSharp.PaddleOCR设计的线程安全模板,示例: SafePaddleOCR safePaddleOCR = new SafePaddleOCR(); string imgPath = @"DimTechStudio-Logo.png"; var res = safePaddleOCR.Run(imgPath); Console.WriteLine($"res: {res.data.Text}"); |
|
BotSharp.Plugin.PaddleSharp
Package Description |
|
HHO.LV.OCR
Library packed for OCR |
GitHub repositories (4)
Showing the top 4 popular GitHub repositories that depend on Sdcb.PaddleOCR:
Repository | Stars |
---|---|
babalae/better-genshin-impact
📦BetterGI · 更好的原神 - 自动拾取 | 自动剧情 | 全自动钓鱼(AI) | 全自动七圣召唤 | 自动伐木 | 自动刷本 - UI Automation Testing Tools For Genshin Impact
|
|
SciSharp/BotSharp
The AI Agent Framework in .NET
|
|
sdcb/PaddleSharp
.NET/C# binding for Baidu paddle inference library and PaddleOCR
|
|
axzxs2001/Asp.NetCoreExperiment
原来所有项目都移动到**OleVersion**目录下进行保留。新的案例装以.net 5.0为主,一部分对以前案例进行升级,一部分将以前的工作经验总结出来,以供大家参考!
|
Version | Downloads | Last updated |
---|---|---|
2.7.0.3 | 1,589 | 6/25/2024 |
2.7.0.2 | 321 | 6/14/2024 |
2.7.0.1 | 5,573 | 1/15/2024 |
2.7.0 | 5,477 | 8/14/2023 |
2.7.0-preview.1 | 109 | 8/10/2023 |
2.6.0.6-preview.8 | 129 | 8/5/2023 |
2.6.0.6-preview.7 | 73 | 8/5/2023 |
2.6.0.6-preview.6 | 89 | 8/4/2023 |
2.6.0.6-preview.5 | 135 | 7/16/2023 |
2.6.0.6-preview.4 | 118 | 7/11/2023 |
2.6.0.6-preview.3 | 84 | 7/10/2023 |
2.6.0.6-preview.1 | 110 | 7/6/2023 |
2.6.0.5 | 5,315 | 6/17/2023 |
2.6.0.4 | 782 | 5/4/2023 |
2.6.0.3 | 489 | 5/3/2023 |
2.6.0.2 | 908 | 3/31/2023 |
2.6.0.1 | 2,199 | 12/8/2022 |
2.6.0 | 1,857 | 9/19/2022 |
2.6.0-preview6 | 212 | 9/10/2022 |
2.6.0-preview5 | 156 | 9/10/2022 |
2.6.0-preview4 | 154 | 8/27/2022 |
2.6.0-preview3 | 154 | 8/24/2022 |
2.5.0 | 3,633 | 8/1/2022 |
2.3.0 | 914 | 6/27/2022 |
2.2.2 | 2,067 | 2/18/2022 |