Text Extraction from Image Using Machine Learning Software

Text extraction from textual images captured or scanned documents using machine learning is a burgeoning field at the intersection of computer vision and natural language processing. This technology leverages advanced machine learning, object recognition algorithms, advanced graphics software, deep and dark web, and neural network architectures to accurately identify and extract textual information from images, and scanned paper documents, ranging from handwritten notes and printed text to complex typography in diverse contexts. By employing various machine learning technologies such as optical character recognition (OCR) and deep learning, it enables automated and efficient conversion of visual scene text detection into editable and searchable structured data therein and object detection.

In this evolving landscape, researchers and practitioners continually strive to improve accuracy, speed, and versatility, making text detection and extraction from images, machine-readable data, and scanned document a pivotal component in applications like printed document digitization, content indexing, translation, and accessibility enhancement.

In this article, we will discuss how you can extract text from images using IronOCR, an OCR Library powered by powerful Machine Learning algorithms and text-related features. Text extraction, also known as keyword extraction, is based on machine learning to automatically scan and extract relevant or basic words and phrases from unstructured data or the company's central database.

How to extract text from an image using machine learning?

1. Download the C# library for text extraction from images.
2. Load a particular image by instantiating the OcrInput object for scene text recognition.
3. Extract data from the image using ocrTesseract.Read method.
4. Print the extracted text in the console using Console.WriteLine method.
5. Perform OCR on the region of an image using the CropRectangle object.

IronOCR- An OCR(Optical Character Recognition) Library

IronOCR, a prominent and sophisticated optical character recognition (OCR) software, stands at the forefront of text extraction technology from images and documents. Developed by Iron Software, this powerful OCR engine is designed to accurately and efficiently convert scanned images, PDFs, or even photographs of text into editable and searchable digital content. With its adept use of machine learning algorithms and neural networks, IronOCR provides a robust solution for various applications, including data extraction, content indexing, and automation processes that require precise text recognition.

Its ability to handle multiple languages and diverse fonts makes it a versatile tool for both developers and businesses seeking streamlined text recognition algorithm extraction capabilities in their software and applications. You can use IronOCR to automatically scan text using a common text recognition technique that converts unstructured data into a perfectly scanned page using text extraction algorithms.

Installing IronOCR

IronOCR can be installed using NuGet Package Manager, here are the steps to install IronOCR.

1. First Create a new C# Visual Studio project or open an existing one.

2. Once the project is created, go to Tools in the top menu and select NuGet Package Manager then select the NuGet Package Manager for Solution.

3. A new window will appear on the screen. Go to the Browse tab and write IronOCR in the search bar.
4. A list of IronOCR packages will appear, select the latest one and click on install.

5. It will take a few seconds based on your internet after that IronOCR is ready to use in your C# project.

Text Detection from Images to Editable and Searchable Data

Using IronOCR you can easily extract the text using image processing techniques and machine learning. In this section, we will discuss how to extract text from images using IronOCR.

using IronOcr;
using System;

var ocrTesseract = new IronTesseract();
using (var ocrInput = new OcrInput(@"images\image.png"))
{
    var ocrResult = ocrTesseract.Read(ocrInput);
    Console.WriteLine(ocrResult.Text);
}

This C# code demonstrates the usage of IronOCR, a library for optical character recognition (OCR). Here's a step-by-step explanation:

1. Importing Libraries:

using IronOcr; 
using System;

The code starts by importing the necessary libraries, including IronOcr, which provides the OCR functionality, and the System namespace for general functionalities.

2. Initializing IronTesseract and Loading the Image:

var ocrTesseract = new IronTesseract();

This line creates an instance of IronTesseract, which is the OCR engine provided by IronOCR.

using (var ocrInput = new OcrInput(@"images\image.png"))

An OcrInput object is instantiated with the path to the image to be processed. In this case, the image file is "image.png" in the "images" directory.

3. Performing OCR and Extracting Text:

var ocrResult = ocrTesseract.Read(ocrInput);

This line invokes the Read method of the IronTesseract instance, passing in the OcrInput object. This method performs OCR on the provided image and extracts the text.

4. Displaying the Extracted Text:

Console.WriteLine(ocrResult.Text);

Finally, the extracted text is printed to the console using Console.WriteLine, displaying the OCR result obtained from the image.

This code snippet uses IronOCR to perform OCR on text recognition of the specified image and outputs the extracted text to the console.

Input image

Output

Perform OCR on the specified region on Image

You can also perform OCR on specific regions on the image using IronOCR, here is a code example.

using IronOcr;
using IronSoftware.Drawing;
using System;
var ocrTesseract = new IronTesseract();
using (var ocrInput = new OcrInput())
{
    var ContentArea = new CropRectangle(x: 20, y: 20, width: 400, height: 50);
    ocrInput.AddImage("r3.png", ContentArea);
    var ocrResult = ocrTesseract.Read(ocrInput);
    Console.WriteLine(ocrResult.Text);
}

This C# code utilizes the IronOCR library for optical character recognition (OCR). It first imports the necessary libraries, including IronOCR and System. An IronTesseract instance, the OCR engine, is created. The code sets a specific ContentArea in the image to be processed using a CropRectangle, focusing on a defined region. The image ("r3.png") within this designated area is then added for OCR processing. The OCR engine reads the specified content area, extracts the text, and the resulting text is printed to the console using the Console.WriteLine.

Output

Conclusion

Text extraction from images through machine learning, notably employing optical character recognition (OCR) libraries like IronOCR, signifies a transformative stride at the crossroads of computer vision and natural language processing. This technology, powered by advanced machine learning algorithms and neural networks, accurately deciphers and extracts text from diverse image types, including handwriting, printed text, and intricate typography. Both OCR technology and deep learning techniques play a pivotal role in efficiently converting visual text into editable and searchable data, serving vital purposes such as document digitization, content indexing, and accessibility enhancement.

IronOCR, as a prominent OCR library, exemplifies the potential of this fusion, excelling in the precise conversion of scanned images and PDFs into digital, editable content across multiple languages and font styles. Its seamless integration into programming languages like C# allows for streamlined implementation, further amplifying the transformative impact of text extraction from images in numerous applications and domains.

To know more about IronOCR and all the related features visit this link here. The complete tutorial on extracting text from images is available at the following link. IronOCR license can be purchased from this link.