IronOCR 與 Dynamsoft OCR 的比較

光学字符识别（OCR）是一种数据输入过程，涉及文本的识别和数字化，包括手写和打印文本。它是一种利用图像分析将打印文本的数字照片转换为其他程序（如文字处理器）可以使用的字母和数字的计算机技术。文本被转换为字符代码，以便可以在计算机上搜索和更改。

虽然过去是一个所有文档都为物理形式的世界，未来可能是一个所有文档都为数字形式的社会，但目前正处于一个变化中。物理和数字文档在这种过渡状态中共存——因此像OCR这样的技术对于来回转换至关重要。

文档恢复、数据输入和可访问性只是OCR的一部分应用。大多数OCR应用程序来自扫描的文件，尽管也偶尔使用照片。OCR是一个宝贵的时间节省工具，因为重新输入材料通常是唯一的其他选择。以下是一些OCR使用的示例：

• 可编辑的文本文件可以从扫描文件中恢复，包括传真。
• 表格使用其手写内容的近似值进行分类。
• 使用书籍扫描创建可搜索和可编辑的电子书。
• 使用截图照片搜索和更改文本。
• 文字转语音技术用于为视力障碍者阅读书籍。

虽然这些只是OCR的部分应用，但它们展示了这项技术在广泛行业中的多功能性。几乎所有公司中的所有员工每天都大量依赖文档，因此商业用途是OCR系统开发的一个关键考虑因素。

在本文中，我们将比较两种最强大的OCR阅读器：

• IronOCR
• Dynamsoft OCR

IronOCR和Dynamsoft OCR是两个支持扫描图像的转换和PDF文件的OCR处理的.NET OCR库。仅需几行代码，您即可将图像转换为可搜索的文本。您还可以检索单个单词、字母和段落。

IronOCR — 突出的特點

IronOCR 提供了獨特的功能，可以檢測、讀取和解讀未精確掃描的圖片和 PDF 文件中的文字。IronOCR 提供了最簡便的方法來從文件和照片中提取文字，即使它並非總是最快的，因為它會自動增強和修正低質量的掃描，減少傾斜、變形、背景噪音和透視問題，同時還提高了分辨率和對比度。

IronOCR 允許開發者向其發送單頁或多頁的掃描圖像，它將返回所有的文字、條形碼和 QR 信息。OCR 庫中的一組類為基於 Web、桌面或控制台應用程序添加了 OCR 功能。Tesseract OCR C# 及 net 應用程式可以使用 JPG、PNG、TIFF、PDF、GIF 和 BMP 等格式作為輸入。

IronOCR 的光學字符識別 (光學字符識別) 引擎可以讀取使用多種常見字體、斜體、字重和底線準備的文字。裁剪類使 OCR 能夠迅速且精確地運作。處理多頁文件時，IronOCR 的多執行緒引擎可以加速 OCR。

IronOCR 功能

為了管理 Tesseract，我們使用 IronOCR，因為它具有以下獨特之處：

• 可在純 .NET 中直接使用
• 不需要在您的機器上安裝 Tesseract
• 運行最新的引擎：Tesseract 5 ( 以及 Tesseract 4 和 3)
• 適用於任何 .NET 專案：.NET Framework 4.5 +，.NET Standard 2 + 和 .NET Core 2, 3 和 .NET 5
• 比傳統的 Tesseract 具有更高的準確性和速度
• 支援 Xamarin、Mono、Azure 和 Docker
• 它使用 NuGet 套件管理複雜的 Tesseract 字典系統
• 支援 PDF、多幀 Tiff 和所有主要圖像格式，無需配置
• 可以校正低質量和傾斜的掃描，以從 Tesseract 中獲得最佳結果。

Dynamsoft OCR — 功能

Dynamsoft .NET OCR 庫是一個 .NET 組件，提供快速且可靠的光學字符識別。 它用於在 C# 或 VB.NET 中創建 .NET 桌面應用程序。 您可以簡單地創建代碼，使用基本的 OCR API 將 PDF 或照片中無用的文字轉換為數字文本，以便進行編輯、搜索、歸檔等。

可以通過以下方式獲取來自掃描儀和其他符合 TWAIN 規範的設備的圖像：

• 支持原生、緩衝內存和磁盤文件圖像傳輸機制。
• 使用自動送稿器，可以進行批量掃描。 (ADF)* TWAIN屬性可以用來修改常見的設備功能。
• 可以更改IfAutoFeed, IfAutoScan, Resolution, BitDepth, Brightness, Contrast, Unit, Duplex等功能。
• 支援空白頁面的檢測。
• 允許您更改和保存掃描儀配置檔。

從符合UVC和WIA的網路攝像頭捕獲圖像：

• 在從選定的網路攝像頭捕捉照片時顯示實時視頻。
• 自定義相機設置：亮度、對比度、色調、飽和度、銳度、伽馬、白平衡、背光補償、增益、顏色啟用、縮放、對焦、曝光、光圈、平移、傾斜、翻轉。

強大的圖像載入和查看功能

• 可載入BMP、JPEG、PNG、TIFF和多頁TIFF格式的圖像。
• 支持圖片的放大和縮小。
• 圖像可以從本地驅動器、FTP伺服器、HTTP伺服器或數據庫中檢索。
• 使用最全面的.NET影像組件之一進行BMP、JPEG、PNG和TIFF的圖像解碼。

保存和上傳/下載

• 允許通過文件流讀取和寫入照片。
• 支持將捕獲的照片保存為BMP、JPEG、PNG、TIFF或多頁TIFF格式到本地驅動器、網頁伺服器或數據庫。
• 支持RLE、G3/G4、LZW、PackBits和TIFF壓縮。
• 支持HTTPS上傳和下載。
• 支持BMP、JPEG、PNG和TIFF圖像編碼的最全面的.NET影像組件之一。
• 允許將新獲得的照片附加到現有的TIFF文件中。

從掃描的 PDF 或其他圖像中讀取文字在 ASP.NET (光學字符識別)

在當今快速變遷的世界，客戶希望工作能夠迅速完成。擁有緊急項目的客戶經常聯繫我們。如果項目涉及掃描包含圖像的文件，我們的技術可以輕鬆識別圖像內容並將其轉換為文本。光學字符識別 (光學字符識別) 節省公司時間和金錢，同時減少數據輸入錯誤。

使用 IronOCR

IronOCR 使用 IronOcr.IronTesseract 類別來執行其 OCR 轉換。

在這個基本示例中，我們使用 IronOcr.IronTesseract 類別從圖像中讀取文字，並自動將結果以字串的形式返回。

// PM> Install-Package IronOcr
using IronOcr;
var Result = new IronTesseract().Read(@"img\Screenshot.png");
Console.WriteLine(Result.Text);

// PM> Install-Package IronOcr
using IronOcr;
var Result = new IronTesseract().Read(@"img\Screenshot.png");
Console.WriteLine(Result.Text);

' PM> Install-Package IronOcr
Imports IronOcr
Private Result = (New IronTesseract()).Read("img\Screenshot.png")
Console.WriteLine(Result.Text)

因此，以下段落是百分之百準確的：

IronOCR 簡單範例

在這個簡單範例中，我們將測試我們的 C# OCR 庫從 PNG 圖像中讀取文字的準確性。這是一個非常基本的測試，但隨著教程的進行，情況將會變得更加複雜。

The quick brown fox jumps over the lazy dog

雖然從表面上看可能很簡單，但在這些表面之下有很多複雜的操作正在進行：掃描圖像的對齊、質量和解析度，查看其屬性，優化OCR引擎，最後如同人類一般讀取文字。

對機器來說，OCR是一項困難的任務，讀取速度可能和人類相當。換句話說，OCR不是一個快速的過程。不過在這種情況下，這樣絕對是正確的。

在大多數實際情況中，開發人員會希望他們的項目能夠盡可能快地運行。在這種情況下，我們建議您使用 IronOCR 附加命名空間的 OcrInput 和 IronTesseract 類。

您可以使用 OcrInput 設定 OCR 任務的確切功能，例如：

• JPEG、TIFF、GIF、BMP 和 PNG 只是幾種可以使用的圖像格式
• 匯入整個或部分 PDF 文件
• 增強圖像的對比度、解析度和大小
• 旋轉、掃描噪點、數字噪點、傾斜和負片矯正

IronTesseract

從數百種預先包裝的語言和方言中選擇

• 立即使用 Tesseract 5、4 或 3 OCR 引擎
• 如果我們在查看截圖、片段或整個文件，請指定文件類型
• 識別條碼
• 可搜尋的 PDFs、Hocr HTML、DOM 和字符串都是 OCR 結果的選項

using IronOcr;
var Ocr = new IronTesseract();
using (var Input = new OcrInput(@"img\Potter.tiff")) {
var Result = Ocr.Read(Input);
Console.WriteLine(Result.Text);
}

using IronOcr;
var Ocr = new IronTesseract();
using (var Input = new OcrInput(@"img\Potter.tiff")) {
var Result = Ocr.Read(Input);
Console.WriteLine(Result.Text);
}

Imports IronOcr
Private Ocr = New IronTesseract()
Using Input = New OcrInput("img\Potter.tiff")
Dim Result = Ocr.Read(Input)
Console.WriteLine(Result.Text)
End Using

我們甚至可以在中等質量的掃描上使用這個功能，並且達到100%的準確度。

如你所見，閱讀文本 (以及，如有需要，條碼) 從掃描圖像（如 TIFF）中提取文本相當容易。該 OCR 作業的準確率為 100%。

接下來，我們將嘗試對同一頁面進行較低質量的掃描，這次採用低 DPI 並伴隨大量變形和數位噪音，原始紙張也受到損壞。

這就是 IronOCR 真正比其他 OCR 库（如 Tesseract）更出色的地方，我們會發現其他 OCR 項目避免討論在現實世界中掃描圖像上的 OCR 使用，而不是為了實現 100% OCR 準確度而數字化創建的不現實的“完美”測試案例。

using IronOcr;
var Ocr = new IronTesseract();
using (var Input = new OcrInput(@"img\Potter.LowQuality.tiff"))
{
Input.Deskew(); // removes rotation and perspective
var Result = Ocr.Read(Input);
Console.WriteLine(Result.Text);
}

using IronOcr;
var Ocr = new IronTesseract();
using (var Input = new OcrInput(@"img\Potter.LowQuality.tiff"))
{
Input.Deskew(); // removes rotation and perspective
var Result = Ocr.Read(Input);
Console.WriteLine(Result.Text);
}

Imports IronOcr
Private Ocr = New IronTesseract()
Using Input = New OcrInput("img\Potter.LowQuality.tiff")
Input.Deskew() ' removes rotation and perspective
Dim Result = Ocr.Read(Input)
Console.WriteLine(Result.Text)
End Using

未添加Input.Deskew() 將圖像拉直後，我們得到52.5%的準確率。這還不夠好。

添加Input.Deskew() 帶來了 99.8% 的準確率，幾乎與高品質掃描的OCR一樣準確。

使用 Dynamsoft OCR

我們將展示一些使用 Dynamic Web TWAIN 進行 TWAIN 掃描和客戶端 JavaScript OCR 的代碼片段。

掃描圖像

您可以使用 Dynamic Web TWAIN 的簡單 API 更改掃描設置並從 TWAIN 掃描儀獲取照片。

function acquireImage()
{
DWObject.SelectSourceByIndex(document.getElementById("source").selectedIndex); //select an available TWAIN scanners

    //set scanning settings like pixel type, resolution, ADF etc.
    DWObject.IfShowUI = false; //don't show the user interface of the scanner
    DWObject.PixelType = 1; //scan in gray
    DWObject.Resolution = 300;
    DWObject.IfFeederEnabled = true; //scan from auto feeder
    DWObject.IfDuplexEnabled = false;
    DWObject.IfDisableSourceAfterAcquire = true;

    //acquire images from scanners
    DWObject.AcquireImage();
}

function acquireImage()
{
DWObject.SelectSourceByIndex(document.getElementById("source").selectedIndex); //select an available TWAIN scanners

    //set scanning settings like pixel type, resolution, ADF etc.
    DWObject.IfShowUI = false; //don't show the user interface of the scanner
    DWObject.PixelType = 1; //scan in gray
    DWObject.Resolution = 300;
    DWObject.IfFeederEnabled = true; //scan from auto feeder
    DWObject.IfDuplexEnabled = false;
    DWObject.IfDisableSourceAfterAcquire = true;

    //acquire images from scanners
    DWObject.AcquireImage();
}

Private Function acquireImage() As [function]
DWObject.SelectSourceByIndex(document.getElementById("source").selectedIndex) 'select an available TWAIN scanners

	'set scanning settings like pixel type, resolution, ADF etc.
	DWObject.IfShowUI = False 'don't show the user interface of the scanner
	DWObject.PixelType = 1 'scan in gray
	DWObject.Resolution = 300
	DWObject.IfFeederEnabled = True 'scan from auto feeder
	DWObject.IfDuplexEnabled = False
	DWObject.IfDisableSourceAfterAcquire = True

	'acquire images from scanners
	DWObject.AcquireImage()
End Function

下載OCR專業模組

要使用客戶端的OCR專業模組，您需要在

請提供內容以進行翻譯。

Make edits to the .js file:

```js
var CurrentPathName = unescape(location.pathname);
CurrentPath = CurrentPathName.substring(0, CurrentPathName.lastIndexOf("/") + 1);
DWObject.Addon.OCRPro.Download(CurrentPath + "Resources/addon/OCRPro.zip", OnSuccess, OnFailure);

Recognize text using OCR

Using the JS OCR recognition API to extract text from scanned images is as simple as inserting the code below.

DWObject.Addon.OCRPro.Recognize(0, GetOCRProInfo, GetErrorInfo); // 0 is the index of the image

DWObject.Addon.OCRPro.Recognize(0, GetOCRProInfo, GetErrorInfo); // 0 is the index of the image

DWObject.Addon.OCRPro.Recognize(0, GetOCRProInfo, GetErrorInfo) ' 0 is the index of the image

Reading Cropped Regions of Images

Both sets of software offer solutions for cropping images for OCR.

Reading cropped regions with IronOCR

Iron's branch of Tesseract OCR is adept at reading specific regions of images, as shown in the following code sample.

We can make use of System.Drawing.Rectangle that is used to describe the exact region of an image to be read in pixels.

When dealing with a standardized form that is filled out, and only a portion of the content changes from case to case, this can be really handy.

Scanning a Section of a Page: We can make use of System.Drawing.Rectangle to designate a region in which we shall read a document. Pixels are always the unit of measurement.

We shall find that this improves speed while also avoiding reading needless text. In this example, we will read a student's name from a central region of a standardized paper.

using IronOcr;
var Ocr = new IronTesseract();
using (var Input = new OcrInput())
{
// a 41% improvement on speed
var ContentArea = new System.Drawing.Rectangle() { X = 215, Y = 1250, Height = 280, Width = 1335 };
Input.AddImage("img/ComSci.png", ContentArea);
var Result = Ocr.Read(Input);
Console.WriteLine(Result.Text);
}

using IronOcr;
var Ocr = new IronTesseract();
using (var Input = new OcrInput())
{
// a 41% improvement on speed
var ContentArea = new System.Drawing.Rectangle() { X = 215, Y = 1250, Height = 280, Width = 1335 };
Input.AddImage("img/ComSci.png", ContentArea);
var Result = Ocr.Read(Input);
Console.WriteLine(Result.Text);
}

Imports IronOcr
Private Ocr = New IronTesseract()
Using Input = New OcrInput()
' a 41% improvement on speed
Dim ContentArea = New System.Drawing.Rectangle() With {
	.X = 215,
	.Y = 1250,
	.Height = 280,
	.Width = 1335
}
Input.AddImage("img/ComSci.png", ContentArea)
Dim Result = Ocr.Read(Input)
Console.WriteLine(Result.Text)
End Using

This results in a 41 percent boost in speed, while also allowing us to be more specific. This is extremely valuable for .NET OCR applications involving documents that are comparable and consistent, including invoices, receipts, checks, forms, expense claims, and so on.

When reading PDFs, ContentAreas (OCR cropping) is also supported.

Reading cropped regions with Dynamsoft OCR

To begin, launch Visual Studio and build a new C# Windows Forms Application, or open an existing one.

We will need to include DynamicDotNetTWAIN.dll, DynamicOCR.dll, and the appropriate language package. To do so, navigate to Tools -> Choose Toolbox Items, then to the.NET Framework Components tab, click the Browse... button, and locate DynamicDotNetTWAIN.dll in "..Program Files (x86)DynamsoftDynamic.NET TWAIN 4.3 TrialBinv4.0" or v2.0 (depends on the .NET Framework version you are using). Click the OK button. The DynamicDotNetTwain component will then appear in the Toolbox dialog (under the View menu), as illustrated in the accompanying image.

Right-click the project file in Solution Explorer and select Add-> Existing Item... Then, in the file type filter's drop-down list, select All Files. Navigate to  “..\Program Files (x86)\Dynamsoft\Dynamic .NET TWAIN 4.3 Trial\Bin\OCRResources” to add items to the project folder. The .NET TWAIN component can then be dragged and dropped onto the form.

This is the code for clicking the LoadImage button:

private void button1_Click(object sender, EventArgs e) { OpenFileDialog filedlg = new OpenFileDialog(); if (filedlg.ShowDialog() == DialogResult.OK) { dynamicDotNetTwain1.LoadImage(filedlg.FileName);
// choose an image from your local disk and load it into Dynamic .NET TWAIN
} }

We can now attempt to OCR the loaded image and turn it into a searchable text file.

private void dynamicDotNetTwain1_OnImageAreaSelected(short sImageIndex, int left, int top, int right, int bottom) { dynamicDotNetTwain1.OCRTessDataPath = "../../"; // the path of the language package (tessdata)
dynamicDotNetTwain1.OCRLanguage = "eng";
// the language type
dynamicDotNetTwain1.OCRDllPath = "../../";
//the relative path of the OCR DLL file
dynamicDotNetTwain1.OCRResultFormat = Dynamsoft.DotNet.TWAIN.OCR.ResultFormat.Text; byte [] sbytes = dynamicDotNetTwain1.OCR(dynamicDotNetTwain1.CurrentImageIndexInBuffer, left, top, right, bottom);
// OCR the selected area of the image
if (sbytes != null) { SaveFileDialog filedlg = new SaveFileDialog(); filedlg.Filter = "Text File(*.txt) *.txt"; if (filedlg.ShowDialog() == DialogResult.OK) { FileStream fs = File.OpenWrite(filedlg.FileName); fs.Write(sbytes, 0, sbytes.Length);
//save the OCR result as a text file
fs.Close(); } MessageBox.Show("OCR successful"); } else { MessageBox.Show(dynamicDotNetTwain1.ErrorString); } }

private void button1_Click(object sender, EventArgs e) { OpenFileDialog filedlg = new OpenFileDialog(); if (filedlg.ShowDialog() == DialogResult.OK) { dynamicDotNetTwain1.LoadImage(filedlg.FileName);
// choose an image from your local disk and load it into Dynamic .NET TWAIN
} }

We can now attempt to OCR the loaded image and turn it into a searchable text file.

private void dynamicDotNetTwain1_OnImageAreaSelected(short sImageIndex, int left, int top, int right, int bottom) { dynamicDotNetTwain1.OCRTessDataPath = "../../"; // the path of the language package (tessdata)
dynamicDotNetTwain1.OCRLanguage = "eng";
// the language type
dynamicDotNetTwain1.OCRDllPath = "../../";
//the relative path of the OCR DLL file
dynamicDotNetTwain1.OCRResultFormat = Dynamsoft.DotNet.TWAIN.OCR.ResultFormat.Text; byte [] sbytes = dynamicDotNetTwain1.OCR(dynamicDotNetTwain1.CurrentImageIndexInBuffer, left, top, right, bottom);
// OCR the selected area of the image
if (sbytes != null) { SaveFileDialog filedlg = new SaveFileDialog(); filedlg.Filter = "Text File(*.txt) *.txt"; if (filedlg.ShowDialog() == DialogResult.OK) { FileStream fs = File.OpenWrite(filedlg.FileName); fs.Write(sbytes, 0, sbytes.Length);
//save the OCR result as a text file
fs.Close(); } MessageBox.Show("OCR successful"); } else { MessageBox.Show(dynamicDotNetTwain1.ErrorString); } }

Private Sub button1_Click(ByVal sender As Object, ByVal e As EventArgs)
	Dim filedlg As New OpenFileDialog()
	If filedlg.ShowDialog() = DialogResult.OK Then
		dynamicDotNetTwain1.LoadImage(filedlg.FileName)
' choose an image from your local disk and load it into Dynamic .NET TWAIN
	End If
End Sub

We can now attempt [to] OCR the loaded image [and] turn it into a searchable text file.private Sub dynamicDotNetTwain1_OnImageAreaSelected(ByVal sImageIndex As Short, ByVal left As Integer, ByVal top As Integer, ByVal right As Integer, ByVal bottom As Integer)
	dynamicDotNetTwain1.OCRTessDataPath = "../../" ' the path of the language package (tessdata)
dynamicDotNetTwain1.OCRLanguage = "eng"
' the language type
dynamicDotNetTwain1.OCRDllPath = "../../"
'the relative path of the OCR DLL file
dynamicDotNetTwain1.OCRResultFormat = Dynamsoft.DotNet.TWAIN.OCR.ResultFormat.Text
Dim sbytes() As Byte = dynamicDotNetTwain1.OCR(dynamicDotNetTwain1.CurrentImageIndexInBuffer, left, top, right, bottom)
' OCR the selected area of the image
If sbytes IsNot Nothing Then
	Dim filedlg As New SaveFileDialog()
	filedlg.Filter = "Text File(*.txt) *.txt"
	If filedlg.ShowDialog() = DialogResult.OK Then
		Dim fs As FileStream = File.OpenWrite(filedlg.FileName)
		fs.Write(sbytes, 0, sbytes.Length)
'save the OCR result as a text file
fs.Close()
	End If
	MessageBox.Show("OCR successful")
Else
	MessageBox.Show(dynamicDotNetTwain1.ErrorString)
End If
End Sub

This is how the application looks.

Image Performance Tuning

The quality of the input image is the most crucial determinant in the speed of an OCR task. The lower the background noise and the higher the dpi, with a great goal value of around 200 dpi, the faster and more accurate the OCR output.

Image Processing Techniques for Dynamsoft OCR

We need to use OCR in a variety of situations, such as scanning a credit card number with our phone or extracting text from paper documents. OCR capabilities are included in Dynamsoft Label Recognition (DLR) and Dynamic Web TWAIN (DWT).

Although they can do an excellent job in general, we can improve the results by using various image processing techniques.

Lighten/remove shadows

Poor illumination may have an impact on the OCR result. To improve the outcome, we can whiten photos or eliminate shadows from images.

Invert

Because the OCR engine is often trained on text in dark colors, text in light colors can be harder to discover and recognize.

It will be easier to recognize if we invert its color

To perform the inversion, we can use the GrayscaleTransformationModes parameter in DLR.

Here are the JSON settings:

"GrayscaleTransformationModes": [
    {
        "Mode": "DLR_GTM_INVERTED"
    }
]

"GrayscaleTransformationModes": [
    {
        "Mode": "DLR_GTM_INVERTED"
    }
]

'INSTANT VB TODO TASK: The following line uses invalid syntax:
'"GrayscaleTransformationModes": [{ "Mode": "DLR_GTM_INVERTED" }]

DLR .net’s reading result:

Rescale

If the letter height is too low, the OCR engine may not produce a good result. In general, the image should have a DPI of at least 300.

There is a ScaleUpModes parameter in DLR 1.1 that allows you to scale up letters. We may, of course, scale the image ourselves.

Reading the image directly yields the incorrect result:

After scaling up the image x2, the result is correct:

Deskew

It is fine if the text is a little distorted. However, if it is overly skewed, the outcome will be adversely altered. To improve the outcome, we need to crop the image.

To accomplish this, we can use the Hough Line Transform in OpenCV.

Here is the code to deskew the image above.

#coding=utf-8
import numpy as np
import cv2
import math
from PIL import Image

def deskew():
src = cv2.imread("neg.jpg",cv2.IMREAD_COLOR)
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
kernel = np.ones((5,5),np.uint8)
erode_Img = cv2.erode(gray,kernel)
eroDil = cv2.dilate(erode_Img,kernel) # erode and dilate
showAndWaitKey("eroDil",eroDil)

    canny = cv2.Canny(eroDil,50,150) # edge detection
    showAndWaitKey("canny",canny)

    lines = cv2.HoughLinesP(canny, 0.8, np.pi / 180, 90,minLineLength=100,maxLineGap=10) # Hough Lines Transform
    drawing = np.zeros(src.shape [:], dtype=np.uint8)

    maxY=0
    degree_of_bottomline=0
    index=0
    for line in lines:        
        x1, y1, x2, y2 = line [0]            
        cv2.line(drawing, (x1, y1), (x2, y2), (0, 255, 0), 1, lineType=cv2.LINE_AA)
        k = float(y1-y2)/(x1-x2)
        degree = np.degrees(math.atan(k))
        if index==0:
            maxY=y1
            degree_of_bottomline=degree # take the degree of the line at the bottom
        else:        
            if y1>maxY:
                maxY=y1
                degree_of_bottomline=degree
        index=index+1
    showAndWaitKey("houghP",drawing)

    img=Image.fromarray(src)
    rotateImg = img.rotate(degree_of_bottomline)
    rotateImg_cv = np.array(rotateImg) 
    cv2.imshow("rotateImg",rotateImg_cv)
    cv2.imwrite("deskewed.jpg",rotateImg_cv)
    cv2.waitKey()

def showAndWaitKey(winName,img):
cv2.imshow(winName,img)
cv2.waitKey()

if __name__ == "__main__":              
deskew()

#coding=utf-8
import numpy as np
import cv2
import math
from PIL import Image

def deskew():
src = cv2.imread("neg.jpg",cv2.IMREAD_COLOR)
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
kernel = np.ones((5,5),np.uint8)
erode_Img = cv2.erode(gray,kernel)
eroDil = cv2.dilate(erode_Img,kernel) # erode and dilate
showAndWaitKey("eroDil",eroDil)

    canny = cv2.Canny(eroDil,50,150) # edge detection
    showAndWaitKey("canny",canny)

    lines = cv2.HoughLinesP(canny, 0.8, np.pi / 180, 90,minLineLength=100,maxLineGap=10) # Hough Lines Transform
    drawing = np.zeros(src.shape [:], dtype=np.uint8)

    maxY=0
    degree_of_bottomline=0
    index=0
    for line in lines:        
        x1, y1, x2, y2 = line [0]            
        cv2.line(drawing, (x1, y1), (x2, y2), (0, 255, 0), 1, lineType=cv2.LINE_AA)
        k = float(y1-y2)/(x1-x2)
        degree = np.degrees(math.atan(k))
        if index==0:
            maxY=y1
            degree_of_bottomline=degree # take the degree of the line at the bottom
        else:        
            if y1>maxY:
                maxY=y1
                degree_of_bottomline=degree
        index=index+1
    showAndWaitKey("houghP",drawing)

    img=Image.fromarray(src)
    rotateImg = img.rotate(degree_of_bottomline)
    rotateImg_cv = np.array(rotateImg) 
    cv2.imshow("rotateImg",rotateImg_cv)
    cv2.imwrite("deskewed.jpg",rotateImg_cv)
    cv2.waitKey()

def showAndWaitKey(winName,img):
cv2.imshow(winName,img)
cv2.waitKey()

if __name__ == "__main__":              
deskew()

#coding=utf-8
'INSTANT VB TODO TASK: The following line uses invalid syntax:
'import TryCast(numpy, np) import cv2 import math from PIL import Image def deskew(): src = cv2.imread("neg.jpg",cv2.IMREAD_COLOR) gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) kernel = np.ones((5,5),np.uint8) erode_Img = cv2.erode(gray,kernel) eroDil = cv2.dilate(erode_Img,kernel) # erode @and dilate showAndWaitKey("eroDil",eroDil) canny = cv2.Canny(eroDil,50,150) # edge detection showAndWaitKey("canny",canny) lines = cv2.HoughLinesP(canny, 0.8, np.pi / 180, 90,minLineLength=100,maxLineGap=10) # Hough Lines Transform drawing = np.zeros(src.shape [:], dtype=np.uint8) maxY=0 degree_of_bottomline=0 index=0 for line in lines: x1, y1, x2, y2 = line [0] cv2.line(drawing, (x1, y1), (x2, y2), (0, 255, 0), 1, lineType=cv2.LINE_AA) k = float(y1-y2)/(x1-x2) degree = np.degrees(math.atan(k)) if index==0: maxY=y1 degree_of_bottomline=degree # take the degree @of the line at the bottom else: if y1> maxY: maxY=y1 degree_of_bottomline=degree index=index+1 showAndWaitKey("houghP",drawing) img=Image.fromarray(src) rotateImg = img.rotate(degree_of_bottomline) rotateImg_cv = np.array(rotateImg) cv2.imshow("rotateImg",rotateImg_cv) cv2.imwrite("deskewed.jpg",rotateImg_cv) cv2.waitKey() def showAndWaitKey(winName,img): cv2.imshow(winName,img) cv2.waitKey() if __name__ == "__main__": deskew()

Lines detected:

Deskewed:

Image Processing Techniques for IronOCR

The quality of the input image is not important here because IronOCR excels at repairing defective documents (though this is time-consuming and will cause your OCR jobs to use more CPU cycles).

Choosing input image formats with less digital noise, such as TIFF or PNG, can also result in speedier outcomes than lossy image formats, such as JPEG.

The image filters listed below can significantly enhance performance:

OcrInput.Rotate (double degrees) — Rotates images clockwise by a specified number of degrees. Negative integers are used for anti-clockwise rotation.

OcrInput.Binarize() — This image filter makes every pixel either black or white, with no in-between. It may improve OCR performance in circumstances where the text-to-background contrast is very low.

OcrInput.ToGrayScale() — This image filter converts every pixel to a grayscale shade. It is unlikely to improve OCR accuracy, but it may increase speed.

OcrInput.Contrast() — Automatically increases contrast. In low-contrast scans, this filter frequently improves OCR speed and accuracy.

OcrInput.DeNoise() — This filter should be used only when noise is expected.

OcrInput.Invert() — Reverses all colors. For example, white becomes black: black becomes white.

OcrInput.Dilate() — Advanced morphology. Dilation is the process of adding pixels to the edges of objects in an image. (Erode's inverse)

OcrInput. Erode() — an advanced morphology function. Erosion is the process of removing pixels from the edges of objects. (Dilate's inverse)

OcrInput. Deskew() — Rotates an image so that it is orthogonal and the right way up. Because Tesseract tolerance for skewed scans can be as low as 5 degrees, this is quite useful for OCR.

DeepCleanBackgroundNoise() — Removes a lot of background noise. Only use this filter if you know there is a lot of background noise in the document because it can reduce OCR accuracy on clear documents and is quite CPU intensive.

OcrInput.EnhanceResolution — Improves the resolution of low-resolution photos. Because of OcrInput, this filter is rarely used. OcrInput and will detect and resolve low resolution automatically.

We may want to use Iron Tesseract to speed up OCR on higher-quality scans.

If we're looking for speed, we might start here and subsequently turn features back on until the proper balance is struck.

using IronOcr;
var Ocr = new IronTesseract();
// Configure for speed
Ocr.Configuration.BlackListCharacters = "~`$#^*_}{][\\";
Ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto;
Ocr.Configuration.TesseractVersion = TesseractVersion.Tesseract5;
Ocr.Configuration.EngineMode = TesseractEngineMode.LstmOnly;
Ocr.Language = OcrLanguage.EnglishFast;
using (var Input = new OcrInput(@"img\Potter.tiff"))
{
    var Result = Ocr.Read(Input);
    Console.WriteLine(Result.Text);
}

using IronOcr;
var Ocr = new IronTesseract();
// Configure for speed
Ocr.Configuration.BlackListCharacters = "~`$#^*_}{][\\";
Ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto;
Ocr.Configuration.TesseractVersion = TesseractVersion.Tesseract5;
Ocr.Configuration.EngineMode = TesseractEngineMode.LstmOnly;
Ocr.Language = OcrLanguage.EnglishFast;
using (var Input = new OcrInput(@"img\Potter.tiff"))
{
    var Result = Ocr.Read(Input);
    Console.WriteLine(Result.Text);
}

Imports IronOcr
Private Ocr = New IronTesseract()
' Configure for speed
Ocr.Configuration.BlackListCharacters = "~`$#^*_}{][\"
Ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto
Ocr.Configuration.TesseractVersion = TesseractVersion.Tesseract5
Ocr.Configuration.EngineMode = TesseractEngineMode.LstmOnly
Ocr.Language = OcrLanguage.EnglishFast
Using Input = New OcrInput("img\Potter.tiff")
	Dim Result = Ocr.Read(Input)
	Console.WriteLine(Result.Text)
End Using

This result is 99.8% accurate compared to the baseline of 100% — but 35% faster.

Licensing and Pricing

Dynamsoft Licensing and Pricing

Per year license. All rates include one year of maintenance, which includes free software upgrades and premium support.

Dynamsoft offers two types of licenses:

Per client device license

The "One Client Device License" provides access to a same-origin Application (same protocol, same host, and same port) to use the software's features from a single client device. An inactive client device is one that has not accessed any software capability for 90 days in a row. An inactive client device's license seat will be instantly freed and made available for usage by any other active client device. When you reach the maximum number of license seats allowed, Dynamsoft will give you an extra 10% of your client device allowance for emergency use. Once the additional client device allowance has been depleted, no new client devices can access and use the software until there are available license seats again. Please keep in mind that exceeding your client device allowance has no effect on any client devices that have already been licensed.

Per-server license

To deploy the application to a single server, a "One Server License" is required. Servers refer to both physical and virtual servers and include, but are not limited to, production servers, failover servers, development servers that are also used for testing, quality assurance servers, testing servers, and staging servers, all of which require a license. Additional licenses are not required for continuous integration servers (build servers) or localhost development servers. The per-server license is only valid for on-premises server installations, and not for cloud deployments.

Pricing for Dynamsoft OCR starts at USD 1,249/year.

IronOCR Licensing and Pricing

As developers, we all want to accomplish our projects with the least amount of money and resources possible — budgeting is critical. Examine the chart to determine which license is best suited to your requirements and budget.

IronOCR provides licenses with a customizable number of developers, projects, and locations, allowing you to fulfill the needs of your project while only paying for the coverage you require.

IronOCR licensing keys enable you to publish your product without a watermark.

Licenses start from $749 and include one year of support and upgrades.

You can also use a trial license key to try IronOCR for free.

Conclusion

Tesseract OCR on Mac, Windows, Linux, Azure OCR, and Docker are all available with IronOCR for C#. .NET Framework 4.0 or above is required,  .NET Standard 2.0+, .NET Core 2.0+, .NET 5, Mono for macOS and Linux, and Xamarin for macOS are all examples of cross-platform development. IronOCR also uses the latest Tesseract 5 engine to read text, barcodes, and QR codes from all major image and PDF formats. In minutes, this library adds OCR functionality to your desktop, console, or web apps! The OCR can also read PDFs and multi-page TIFFs, and it can be saved as a searchable PDF document or XHTML in any OCR Scan. Plain text, barcode data, and an OCR result class encompassing paragraphs, lines, words, and characters are among its data output choices. It is available in 125 languages, including Arabic, Chinese, English, Finnish, French, German, Hebrew, Italian, Japanese, Korean, Portuguese, Russian, and Spanish, but keep in mind that bespoke language packs can also be generated.

The Dynamic .NET TWAIN OCR add-on is a quick and reliable .NET component for Optical Character Recognition that you can use in WinForms and WPF applications written in C# or VB .NET. You can scan documents or capture photos from webcams using Dynamic .NET TWAIN's image capture module, and then conduct OCR on the images to convert the text in the images to text, searchable PDF files, or strings. Multiple Asian languages, as well as Arabic, are offered in addition to English.

IronOCR offers better licensing than Dynamsoft OCR; IronOcr starts at $749 with one year free, while Dynamsoft starts at $1249 with a free trial. IronOCR also offers licenses for multiple users, while with Dynamsoft, you only get one license per user.

While both sets of software aim at offering the best performance in terms of OCR readings of barcodes, image to text, and image to text, IronOCR stands out in that it shines its light even on images that are in pretty bad shape. It automatically puts in place its sophisticated tuning methods to give you the best OCR results. IronOCR also makes use of Tesseract to give you optimal results with little or no errors.

Iron Software is also offering its customers and users the option to grab its entire suite of software in just two clicks. This means that for the price of two of the components in the Iron Software suite, you can currently get all five components and uninterrupted support.