In the 1990s, considerable advancements were made in the recognition of multilingual printed text. However, the problems with handwritten symbols presented a barrier to conventional character recognition techniques, making the decoding of a handwritten text much more complicated. The main problem was that robots, unlike humans, could not identify the boundaries between characters.

Moreover, the problem was compounded by the lack of technical preparation. Training modern neural networks has been difficult because they require processing power and memory capacities that were previously unimaginable. Less resource-intensive methods did not deliver the expected performance and training results.

Notwithstanding these obstacles, text recognition technology became a revolutionary tool. These changes were based on the Optical Character Recognition (OCR). It streamlined many business processes and digitised multiple documents.

So, what is OCR?

OCR is a technique that converts text from images into formats that computers can read. Think of scanning a receipt or form; the scan is saved on our computer as an image file. Traditional word processors cannot search or modify such files, but OCR easily converts such images into a text document.

Extracting data from printed materials, including forms, invoices, scanned legal documents, and printed contracts, is a standard part of business processes—large volumes of paper-based work require a lot of processing and storage space, as well as the time to handle. While paperless document processing has advanced over time, there are still issues with the OCR process that need human intervention. Today, however, handwritten text and digital efficiency are merging, making the previously difficult task of recognising handwritten language a simplified reality.

How it’s made?

When we digitise documents, we generate image files with embedded text. However, the problem is that word processing software is unable to process textual content embedded in such images. OCR technology solves this problem by converting the image into textual data that any office application can read. This data may then be used for analysis, optimisation and process automation, as well as efficiency enhancement.

OCR technology involves several stages:

  1. Image capture: the scanner captures all the symbols in the document and converts them into a seamless image. The OCR engine analyses this image, categorising light areas as the background and dark areas as the text.
  2. Pre-processing: the OCR engine removes extraneous elements from the image in order to read the text for recognition. This step includes techniques such as text alignment, enhancing contrast smoothness, as well as removing unnecessary frames and lines from scanned images.
  3. Text recognition: two main types of OCR algorithms or software processes are used for text recognition: template matching and feature recognition.
  4. Final processing: after the analysis, the system converts the retrieved textual data into a digital file.

Today, OCR technology has significantly changed the way we handle and understand printed documents. OCR software uses many pre-existing font and text image templates to accurately identify and interpret characters. Nevertheless, its limitations become evident when confronted with diverse handwritten symbols due to multiple differences in handwriting styles.

Intelligent Character Recognition (ICR) is also used in addition to OCR technology. Using advanced neural networks, ICR simulates human reading and recognition capabilities. This system uses a multi‑level analysis to examine text, iteratively processing images and recognising features such as curves, lines, intersections, and loops. ICR demonstrates exceptional efficiency by delivering results within seconds, even at the character level.

There are also Intelligent Word Recognition systems that work similarly to ICR, except they analyse whole words without the need for initial character separation.

In addition to text recognition, OCR also supports character recognition, allowing one to read brands, watermarks, and other notes included in documents. This ability helps us to better understand the material.

One of the best features about OCR is that it makes the text readable, which means companies can use old and new documents to create searchable knowledge bases.

As OCR improves, it becomes indispensable to any artificial intelligence (AI) system. OCR can already read and recognise car number plates and company logos on social media. So, all AI applications using OCR give businesses more power, reduce costs and improve the quality of customer service.

Today, OCR technology is going beyond simple character recognition, opening the door to the day when any textual data can be seamlessly processed to boost productivity in a variety of sectors.

In the translation sector, OCR technology is also very helpful as it can do more than just recognise characters; it can also make the translation process more efficient and effective.

It significantly reduces the human intervention required for data input by automating the conversion of printed or handwritten text into digital form. Instead of wasting time on tedious and time‑consuming typesetting or formatting, translators can quickly extract text from a variety of sources, such as scanned documents, photos, and PDFs. The speed of text extraction accelerates the translation process, allowing translators to be more productive on tight deadlines.