Everything you Need to Know About how OCR Technology Works

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In today’s highly competitive era, businesses tend to look for solutions that best serve their customers. Optical Character Recognition (OCR) is one such technology that has totally changed the way companies collect data and use it to provide the customer with a seamless service. The main purpose of OCR technology is to read data from paper-based documents and convert them into digital files. 

With recent developments, OCR technology is able to generate machine-readable output (text) that can be converted into various languages as well. It saves businesses the time and cost of manually entering customer data and investing a good deal of resources. With OCR technology emerging, its total market size is forecasted to reach almost $5.27 billion in 2025. Let’s have a detailed look at how OCR works.    

How does OCR Technology Work?

Optical Character Recognition software performs initial processing on the document, converts the text into features to extract them, and outputs the digital text in the form of a PDF, document file, etc.

Stage 1 – Preprocessing the Document

Since documents come in a wide array of formats, text fonts and languages, it is necessary to perform some kind of preprocessing on them. This helps improve the accuracy of character recognition and reduce the overall turnaround time. Below are listed some methods that are used in the pre-processing stage of OCR.

De-skew Method

The De-skew procedure makes sure the document does not have any redundancies in alignment. This technique makes adjustments to the document horizontally and vertically for an error-free feature extraction.

Binarization of Text

Once the document is taken as an input using the OCR technology, it needs to convert color documents in a binary image – a monochrome image with only two colors: black and white. This way, the text and other features can be easily extracted with lesser computational cost. 

Removing Unnecessary Features 

To make feature extraction as easy as possible, OCR technology removes all irrelevant lines and other fields from the document to reduce the possibility of error. Only text written in readable format is taken into account and the words are recognized as fit for extraction.  

Normalizing Characters

Also called character segmentation, this step divides the documents into certain characters. When it comes to a text document, OCR technology applies segmentation based on the nature of characters. To normalize the characters, the aspect ratio and scale of the document is also changed for an effortless feature extraction. 

Stage 2 – Feature Extraction

Feature extraction is the next step after the document is gone through the preprocessing process. Here, every character is a feature vector – a block of information related to that specific character – that the OCR technology uses to extract data from the document. The feature extraction is carried out using the following techniques: 

  • The feature detection algorithm is purpose-built to analyze only the lines and strokes in a particular character
  • The second method is different from the previous one where it considers the whole character rather than smaller parts like lines and strokes 

Stage 3 – Post-processing 

Once the data i.e. the features are completely extracted from the document, it is time to check them for any potential errors. The OCR engine accuracy will be quite significant in the case if the output data after feature extraction is a single lexicon. The lexicon is the collection/library of all the words that can possibly appear in the document. With OCR technology emerging over the years, many OCR libraries are available online that can reduce the error rate resulting in a lack of a comprehensive lexicon. 

To make error correction, the OCR software uses a method/algorithm called nearest neighbor. This technique is helpful when it comes to finding combinations of words that are related to other words or words that come before or after a specific word.

In sum, OCR technology performs preprocessing to improve the data quality for character recognition, after which feature extraction analyzes and extracts relevant information using pattern recognition. The last step includes error detection and correction of grammar to improve the accuracy of the document. 


Meta: OCR technology does preprocessing on the input data so that features could be easily extracted from the document with a final error correction at the end. 

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