OCR Technology: What Factors are Assisting and Restricting it?

OCR technology has advanced to a considerable extent since it came ashore a couple of years ago. OCR is ideal for decoding and understanding a variety of languages, symbols, and pictures. Not only that, but it even allows you to change the records’ distinctive shape, writing style, and also font color. OCR technology has a broad range of advantages for companies, and it can seamlessly replenish regular business operations.


Current OCR software can recognize a wide variety of documents, photographs, and symbols, allowing you to simplify your workflows. Furthermore, OCR solutions are getting more and more accurate. Any of the most advanced OCR solutions have an accuracy score of over 90%. As a consideration, let us look at the most recent trends in OCR software tools and the variables that are assisting their workflow.


What is OCR Technology?

OCR software that identifies text from typed papers, handwritten notes, or photographs is known as an OCR document scanner. Employees can better transfer documents through their computer networks and libraries using OCR technology. Furthermore, it allows for data processing using powerful methods that can manage even the most complex data.


Which are the Technologies OCR Draws From?

Computer Vision Technology

OCR technology first identifies characters distinctly, thanks to advances in computer vision technology. Following that, image processing and classification technology are used to classify each character. Moreover, OCR technology produces exact results after the successful establishment and completion of these two levels. It should be noted that characters may, however, be very similar to one another in certain situations and may not be identified correctly. As a result, the OCR software necessitates more than just CV technology.

Natural Language Processing Technology

OCR technology differentiates between characters and phrases, sentences and passages, and so on. Further to that, advances in the application of NLP have contributed to the growth of a number of mathematical algorithms that can be used to fix detection errors. Word identification, for example, can be achieved using context even though certain characters are absent.

Deep Learning Technology

To strengthen its scanning functions, OCR employs deep learning techniques. With this innovation, though, it is necessary to scale ML models in order to enhance the optical character recognition procedure. ML models may identify characters with a variety of textual types after proper scaling. Each symbol could be worded differently and in a variety of fonts, but the OCR software can handle it with ease.


You can also identify and fix defects. The OCR software can even disregard letters or characters that don’t get identified. It’s all achieved due to the training of machine learning models when specific patterns are instilled in the template.


OCR Services: What are their Inhibitors?

Letter misinterpretation, missing indecipherable characters, and mixing words from various columns or picture annotations are all examples of faults in OCR scanning performance. Although there are several factors that influence the results of OCR systems, the majority of errors are caused by poor text quality. However, since each letter or icon has a vast variety of formats, styles, and colors, OCR technology still may misunderstand even the high-quality papers.


The following are the drawbacks associated with OCR technology:

Retrieving Structured Data

The identification of organized data may be a major problem for OCR software. Therefore,  OCR technology uses certain machine learning methods to identify this type of data. So, it could be an unwelcome necessity for some businesses.

Colored Backgrounds

OCR system works well for black and white images. This isn’t to say that the vivid context patterns are unidentifiable, but they do make OCR information extraction difficult.

Blurred or Vague Texts

Both humans and machines have a hard time recognizing blurry or vague images. Furthermore, OCR can struggle to correctly classify data from distorted or non-oriented records.

Cursive Forms and Languages

Few languages or styles of writing can be more difficult for OCR services to understand. Like recognizing cursive forms, such as those used in the Arabic language, can be difficult. OCR, although, is capable of understanding the characters of virtually every language on the planet.