What is Optical Character Recognition?

Optical character recognition, or OCR, is an existing technology that enables a user to convert scanned paper documents, PDFs, and images into editable, searchable, and machine-readable text.

OCR uses artificial intelligence and pattern recognition to identify characters inside images and extract the corresponding text. This text will then be used for many functions such as archiving, digitizing, or creating searchable databases.

What Is Handwriting Optical Character Recognition?

Handwriting Recognition is one of the major OCR types that are most commonly used. It is also referred to as Handwritten character recognition (HCR) or handwritten text recognition.

There are nine different methods that can properly identify characters in these documents. These nine techniques are the incremental recognition method, the part-based method, the ensemble method, the convolutional neural network method, the support vector machines method, the semi incremental recognition method, the zoning method, the slope and slant correction method, and the line and word segmentation method.

Incremental Recognition

Refers to the incremental learning of a system over time. These systems are often referred to as online learners. What happens is the computer recognizes characters of a user’s writing, learns about the user’s style of writing, and can identify and recognize the user’s handwriting faster as time goes on, which enables the system to become increasingly more accurate.

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Part-Based Recognition Method

Recognizes handwritten characters without knowing the exact location in the image and the characters may appear at different locations in the image.

Ensemble Recognition Method

Groups together similar handwritten words or characters by combining the predictions of different weaker classifiers to produce a stronger learning algorithm that will improve the accuracy of the result.

Convolutional Neural Networks

Are a specific type of neural network that was developed to solve problems involving images. CNNs consist of several layers of neurons that become joined together. Each layer takes a different feature of the image and combines it with features from previous layers to create a higher-level representation of the image. CNNs also perform well in image classification tasks

Support Vector Machines

Use algorithms to identify characters that are similar between the two input patterns used in data points. It can also classify handwritten digits from 0 to 9.

Semi Incremental Recognition

This method is a two-stage process that recognizes characters. First, it recognizes the strokes that compose each character, and then it uses each stroke to identify the character. This approach is faster and more tolerant to variations in stroke order. And is also better at handling cursive scripts.


This recognition method allows the restriction of specific words, addresses, and dates without affecting other texts. Once zoning is enabled it only recognizes the requested attribute within a specific zone or the entire text, if not specified. This can be useful in security aspects or to administer control over certain parts of a document. You can draw a line around the area that contains the text you want to recognize as sensitive and the software will then mark this up with a special color so it’s easy to spot later on when reviewing the document.

Slope and Slant Correction

The method corrects the slope of letters. The algorithm uses a formula to calculate the slope of each letter and correct it based on its context. This method is often used to identify characters.

Line and Word Segmentation

Identifies lines, letters, words, and other symbols in a scanned document. This is executed by one of three methods. The methods are the Brute force method, optimization-based method, and inductive learning method. The brute force method involves testing all possible combinations of line breaks and word breaks. It is computationally expensive but it guarantees that it will find all possible solutions if they exist. Optimization-based methods use heuristics to solve partial problems which are then combined to produce an overall solution. The inductive learning method uses machine learning techniques to learn from example documents and then utilizes that knowledge onto new documents that have not been seen before.

Out of those nine methods, the method with the best accuracy in extracting and identifying handwritten documents is the convolutional neural network (CNN) method. Even though handwriting recognition is a lot more difficult than the traditional OCR method.

The Process of Optical Character Recognition

OCR technology works by analyzing a document or image and extracting the characters within it. The entire OCR extraction process consists of three steps to complete each extraction of data.

The pre-processing step consists of removing any background noise or extraneous information in an image or document that could interfere with the OCR data extraction process. This involves deskewing, zoning, despeckling, binarization, line removal, character isolation, and script recognition.

  • Deskewing – aligns images that have been scanned out of alignment.
  • Zoning – splits data into different areas like columns and captions.
  • Despeckling – removes spots in documents and images, as well as smoothes the edges of them.
  • Binarisation – converts colors to black and white within images to separate texts from their background for data recognition.
  • Line removal – clears extra lines and spaces to optimize data.
  • Character Isolation – often identified as segmentation, divides image artifacts into different characters.
  • Script recognition – acknowledges different scripts within a document to determine the right data is captured at the right time.

Once the image or document has been pre-processed, it’s then fed into an OCR engine to begin the character recognition process.

The Character Recognition process consists of characters being assessed in two ways. They are Matrix Matching and Feature Extraction.

  • Matrix matching is pattern recognition that compares character images to glyphs that are stored. This is best used on characters with standard fonts that are typically non-fancy.
  • Feature Extraction identifies the loops, lines, intersections, and direction that creates an efficient character recognition system.

The last step in the OCR data extraction process is called Post Processing. Once the data in this step is processed the level of accuracy increases from techniques used like Lexicon, Natural Language Processing (NLP), and Database Lookups. These techniques help make sure the images are ready to use.

Afterward, the results are exported in readable formats for application usage like pdfs and issued to the client.

What is OCR

An explaining Video by Eye on Tech

How Is Optical Character Recognition Used

OCR application is used in document management, data entry, and form processing.

Document Management is one most used. It uses OCR to convert paper documents into digital files. As well as produce searchable databases of documents making it easy to find specific information.

Data Entry uses OCR to store data entries such as a listing of names and addresses in a paper document, and you can use OCR to convert this into an electronic spreadsheet.

Forms Processing is used to automate forms processing. This is mostly executed in businesses that receive a significant number of forms regularly.

OCR Used In Different Industries

Businesses can use OCR to automate data for documentation like invoices, receipts, and more. This enables them to save lots of time, and money and cut back the requirement needed for manual data entry.


The government uses OCR to convert scanned documents like affidavits, wills, judgments, filings, and other legal documents into digital format for storage and retrieval. These documents can also be searched digitally.


Healthcare providers use OCR to reduce manual paperwork by processing patient health records and verifying insurance claims and payment records. A patient’s entire medical record of tests, X-rays, diagnostics, treatments, conditions and diseases are all scanned and stored with the help of OCR. This helps improve patient care by providing quick, easy, and searchable access to medical information.


OCR can be used to process transcripts of student grades and convert scanned textbooks into digital format, making them more accessible for students with disabilities. Also, OCR can easily create searchable databases for educational resources.


Logistics is a hectic industry. OCR can help businesses in this industry organize by keeping track of T-shirt labels, receipts, invoices, and other documents.


Banks use OCR to process and manage checks and verify documents for paperwork such as insurance, loans, transfers, and other online transactions. This technology helps prevent fraud by verifying the authenticity of documents and financial transactions while reducing processing time. The most common process used in OCR for banking is validating signatures, scanning handwriting, and clearing checks.


OCR can scan sheet music to make notations available online, and convert them into a sound that allows a computer or cellphone to read and play the sheet music.

Supply Chain

In other industries like food, drink, cosmetics, and pharmaceutical the OCR process allows for the proper maintenance of appropriate drug storage, equipment, and other consumer products. It lets a user read lot codes, batch codes, expiry dates, and serial numbers to follow products in all stages of their packing cycle. It can even compare and flag errors to ensure a company can locate the item and ensure the item complies with the laws of safety and anti-counterfeiting at any moment.

Miscellaneous Industries

In other businesses, OCR helps sort mail, validate passports, process purchase orders, process claims on a customer and administrative level, and help in the assembly benefits and incentives for employees.

What are the benefits/advantages of Optical Character Recognition?

OCR automatically reads text from images. This is beneficial in doing tasks such as automated data entry, document management, and archiving, which helps transform many businesses by digitizing their process, minimizing manual work, increasing productivity, and reducing labor costs.

Its ability to take data and convert it into machine-readable formats helps businesses become indexed and provides accessibility for people with visual impairments.

What are the disadvantages of OCR?

OCR is complex and limited in many ways. It can become time-consuming and experience difficulty when it scans blurry and low-quality images or documents with multiple pages or documents with a lot of text or intricate designs and layouts.

OCR is also pretty costly for individuals and small businesses and is not 100% accurate. The OCR software can lose data or misinterpret it when converting a scanned document to a digital file. It also almost always requires cleaning up its scanned documents from blemishes, smudges, and other imperfections. It is also unable to support all font types, languages, and texts inside images.

The History Of Optical Character Recognition

OCR technology was originally designed in the 1800s and was built to help the visually blind read. In 1920 an inventor named Gustav Tauschek built an OCR punch card accounting system. And in the 1970s the technology was picked back up by an American inventor named Ray Kurzweil who built the company Kurzweil Computer Products Inc to create software that can transform pictures into text accurately. This venture allowed Ray to invent the Kurzweil Reading Machine, and the algorithm in this machine was capable of recognizing practically any type of text font.

During the 1980s he sold his machine to Xerox, and that machine could read text aloud which is now called a text-to-speech format. By the 1990s historical newspapers were digitized by OCR machines, and even up until today we still use OCR by scanning documents and images in real-time with our smartphones.

What are some future developments for OCR?

OCR technology is continually evolving and improving. Future developments plan to embrace higher accuracy, quicker processing times, as well as the ability for OCR to read text in a wider range of languages.

Additionally, they are plans for OCR technology to mimic the human mind. Generally, algorithms typically rely on patterns to determine their accuracy, and many hope that in the future they won’t have to do that because they would be able to recognize text and determine what it means by themselves.


Advanced Technologies like Optical Character Recognition permits anyone to convert pictures and documents into editable, searchable, and machine-readable text. This tool allows us to effectively organize and produce high-volume documents, eliminate errors, cost and reduce daily manual human tasks of data entry.

By using OCR individuals and businesses can compare and check data and files for verification, errors, or access information. And this accessibility allows ease of advanced analysis so they can learn, improve and produce better and quicker results. Overall, despite OCR’s ability to make a few mistakes, OCR was the first move in transforming analog records into digital records.



Modern speech recognition systems need human input in the form of datasets.

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