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Extracting images from the text can be tiresome because people retype text by hand from an idea. This method becomes time-consuming and stressful when dealing with multiple images. In this case, you can make things more efficient with the help of OCR technology.

To provide automated scanning and writing solutions, OCR technology is gradually increasing in the business communities, academics, and data entry fields. 

The automated OCR technology makes complex document scanning a lot easier. Within seconds, you can extract data from identity cards, receipts, and business documentation. But why is this technology becoming so popular, and how does it work?

This article will discuss the step-by-step working of OCR technology to guide you in its efficiency and functioning. We will also suggest various tools and discuss how you can use them to extract text.

» What is OCR Technology?

Optical Recognition Technology assists users in extracting text from images in a readable form. This technology works with computer systems and hand-held devices as well. These tools efficiently recognize words, phrases, numbers, and special characters.

Nowadays, the OCR software is becoming more accurate than ever. They are trained to process data within seconds under the extensive database provided. But still, these tools work faster than the human eye and hand. Therefore, let's have a look at its works.

» How Does OCR Extract Text from An Image?

OCR works like a human reading and writing text from a digital document or an image. But one significant difference is that a human ignores unwanted information by manually entering everything by hand.

In contrast, OCR technology works slightly differently to scan the text's quality. This technology follows a step-by-step approach to scan text from images which are discussed below:

    • Pre-processing the Image

This is the first function of an OCR technology as it identifies any errors and corrects them with the most relevant information in its database. 

Firstly, it de-skew the image by correcting its alignment and angle to recognize which area it must scan. Then it looks for any columns, charts, or tables in the document. 

Then the binarization process is carried out in which an image or scanned document transforms into a black and white display fragmentation. This process is a relatively accurate way to separate text from white background for precise recognition.

Once the binarization is completed, the OCR technology performs layout analysis to identify rows, paragraph formation, columns, blocks, or other elements.

Finally, the conclusive step of pre-image processing is carried out by adjusting the pixel intensity and making it sharper for a better image. 

This process allows the OCR to make the image clearer and quickly identify the text due to sharp imaging. A normalized image is more vivid and intense than its original version.

    • Segmentation

In this step, the program organizes the document to recognize each line based on words and sentences. The following steps are involved in segmentation, which mainly focuses on text structure. 

In the first segmentation step, the OCR focuses on detecting lines and words adjusted in a paragraph. It separates each paragraph line to create distinction in the written text structure.

The next step involves the identification of scripts based on documents, pages, paragraphs, words, and characters. The segmentation process is crucial when scanning notes, numbers, or mathematical equations.

    • Feature Recognition

Now the OCR technology will recognize the text's patterns and formation, including words and characters. It includes a character's size, height, shape, and structure. This process helps identify capitalized terms and small letters from the software's existing library.

The first process in feature recognition is matrix matching. In this process, the characters scanned by the software are compared with the library of character matrices. 

The OCR model performs a pixel-by-pixel comparison to identify a character with the image it creates that matches the corresponding character.

The following process is to identify the features and patterns of characters from images. This step identifies the numbers and alphabets based on their size, shape, and structure to compare with the existing characters in its library.

This step is vital when we're focused on recognizing characters and numbers written or printed in different formats. The general shape and size of characters, such as the number 5 or letter A, are stored in the OCR library. So, it scans the document to find the most appropriate match pixel-by-pixel.

    • Post-processing for Output

This final step identifies an OCR tool's accuracy in extracting text from an image. 

First of all, the data is detected and fixed if necessary. The spelling mistakes are identified with the same library comparison process.

Then the extracted information is compared with the library of characters to identify any grammatical mistakes for presenting the final output in the form of text. 

This feature is rare in some OCR applications as it is not working correctly to some extent and requires drastic improvements.

» Suggested OCR Tools

Now that we have understood the working of an OCR tool or software, let us discuss how you can extract text from image step-by-step using these efficient OCR tools:


1. Prepostseo

Prepostseo-OCR-tool

This online toolkit provides one of the most efficient OCR tools to scan three images at once for text extraction. The Prepostseo.com image-to-text converter is powered by the latest OCR technology to extract text from any document.

This tool supports almost every type of image format, including the most popular and complex PNG, GIF, BMP, and TIFF formats. If your image includes a specific language, you can scroll down and select from up to 20 different languages.

How to Extract Text from Image with Prepostseo.com?

Select the image you want to scan by clicking on the Upload Picture button. You can directly paste the image or enter the URL so the tool can scan it.

Once you have selected one or more images, click on the Submit button, and the tool will start scanning the images for text, numbers, and characters.

Finally, the scanned text will appear in the output box, and you can download it as a text file or directly copy it to paste anywhere.


2. Onlineocrnow

Onlineocrnow-OCR-tool

This OCR tool is efficient enough to convert images featuring typed text, printed text, or even handwritten text. The nlineocrnow.com implements image-to-text conversion with Machine Learning technology. You can extract text from over 100 different languages by using this tool.

How to Extract Text from Image with Onlineocrnow.com?

In the first step, you must upload an image in PDF, JPG, PNG, GIF, JPEG, or BMP format from your computer's local storage.

This tool will allow you to crop or adjust the image for scanning a specific portion. You can also choose to select one or more images for extracting text.

Once you have made the adjustments, click the Convert button to extract text from the image.

Finally, you can download the extract as a text file.


3. Imagetotext

Imagetotext-OCR-tool

This is one of the most efficient online freeware tools for extracting text from an image. It is powered by AI-based extraction software to bring the most accurate results.

Imagetotext.info has the most straightforward interface to assist the user experience. You can efficiently perform JPG to Word, PDF to text, and PDF to Word conversions with one click. Follow this procedure to extract text from an image.

How to Extract Text from Image with Imagetotext.info?

You can upload an image from the local storage by clicking the Browse button. You can also paste an image URL or directly drag and drop an idea for scanning.

After selecting and uploading the image, click the Submit button, and the tool will scan the image within seconds.

Copy the text from the output box or download it as a document file.


» The Next Generation OCR

The next generation of OCR technology is here, which is way more improved than its first-generation technology back in the 90s. The new generation OCR is powered by machine learning and artificial intelligence, which is focused on automation. 

This new technology expected to be a part of OCR applications is known as Intelligent Document Processing, developed exclusively for extracting text from plain and digital documents.

IDP (short form for Intelligent Document Processing) is way more efficient than human capabilities. It recognizes, organizes, and converts data by itself to make it accessible for users. The following technologies have become a part of OCR to make IDP more efficient and accurate.

    • Machine Learning

The Machine Learning technology in OCR makes the system capable of recognizing patterns and content through a defined set of rules. This process is carried out with a combination of supervised and unsupervised learning.

    • Supervised Learning

Supervised Learning in ML is about compiling data sets to supervise algorithms to make them capable of producing data outcome predictions with higher accuracy. In order to make this possible, a large amount of data, including numbers, alphabets, and prepositions, to achieve accurate and efficient results.

An OCR application's character recognition and language recognition ability depend on ML supervised learning. The requirement for a data set increases, even more when you want an OCR to scan and recognize different languages.

    • Unsupervised Learning

Unsupervised learning is somewhat similar to supervised learning. But instead of labeled data, unsupervised learning follows unlabeled data. This approach helps read the properties which are hard to match with the provided data set.

The example can be taken from scanning receipts by an OCR application. Let's say you need to process some tickets or identification cards. Then you'll need to provide permits and identification cards for structures to OCR to find the similarities between the structure of scanned documents.

    • Artificial Intelligence

Artificial intelligence has become a basis of automation no matter which technology it collaborates with. AI technology embedded within an OCR tool assists the process of automation and makes the software more capable of constantly recognizing data with greater accuracy. 

Moreover, it can help recognize patterns, languages, layouts, and formats. The knowledge database can be created and increased over time to cover all written and printed text types.

The best thing about AI is that it adapts the human-level capabilities to recognize numbers, special characters, and alphabets in a text. In an OCR software, AI allows analyzing all available data to the system, finding correlations, and developing an information-rich knowledge base. 

» Conclusion

In this guideline on extracting text from an image using OCR technology, we explained the functioning of a standard OCR software. We also suggested several tools to extract text from an image. With the combination of NLP and Artificial intelligence, these tools have gained human-level accuracy.

In this rapidly growing era of technology, these tools have dominated the workplaces, businesses, and academic institutes to bring efficient text scanning solutions. You can edit a digital document easily and convert it into any document file format using these tools. 

We hope you find this guide useful, and hopefully, you learned something from it. 

 

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