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OCR technology: fundamentals, applications, and challenges

Janina Horn

Optical Character Recognition (OCR) is a technology that has been used for many decades to automatically recognize printed text and convert it into digital data. ICR extends OCR to recognize handwritten text, here read more about the distinction between OCR, OMR and ICR.

OCR has become a basic technology of workflows for digitization, used in a wide range of application areas. However, recent research shows that end-to-end OCR technology is being replaced in perspective. In our article OCR Free Document Understanding read how in the future OCR technology may become obsolete.

In this article, we'll take an in-depth look at how OCR works, the different areas of application, and the challenges and benefits that come with it. 

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OCR - definition and introduction

OCR (Optical Character Recognition) is a technology that recognizes printed text and converts it into editable digital data. In the process, images or documents are scanned or photographed and analyzed by special algorithms. OCR, as a collective term for these algorithms, extracts the characters they contain and converts them into machine-readable text. 

This text recognition enables the automatic full-text recognition of documents and enables downstream processing steps, such as searching through document texts, extracting the information they contain and enriching images and scans of these paper documents in digital formats.

OCR can be used in various application areas, such as office automation, document management, archiving, word processing, and automatic data entry. 

The accuracy of OCR results can be affected by various factors such as the quality of the source material, the font, the language, and the readability of the characters. Advances in image processing and machine learning technology have led to improvements in OCR accuracy and performance. 

OCR is an important technology that helps businesses and organizations streamline their workflows and increase efficiency.


The way OCR works is based on a complex process that consists of several steps. 

The following components can be part of an OCR

  • Image acquisition: The document or image to be recognized is captured using a scanner, camera, or other imaging system. Here, good image quality is important to ensure the readability of the text.
  • Preprocessing: The captured image is first pre-processed to reduce noise and irregularities. This includes removing noise, correcting skew or distortion, and optimizing contrast and brightness.
  • Text recognition: In this step, the preprocessed text in the image is recognized and converted into machine-readable text. For this purpose, special algorithms and techniques are used that perform character segmentation and character recognition.
  • Character segmentation: The recognized text is divided into individual characters or groups of characters. This step is important to distinguish the individual letters, numbers or symbols from each other.
  • Character Recognition: Each segmented character is analyzed and compared to a character set or model to find the best possible match. Machine learning algorithms, pattern recognition and statistical models are used here.
  • Postprocessing: After character recognition, various post-processing steps are performed. This includes correcting errors, applying text formatting, improving readability and cleaning up the recognized text.
  • Issue: The result of OCR is a machine-readable text that can be used for further processing steps. This can include storage in a database, further processing in other applications, or display on a screen.

Depending on the specific OCR system and the algorithms and techniques used, how OCR works varies. Advances in image processing, machine learning, and artificial intelligence have led to continuous improvements in OCR accuracy and performance.

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OCR benefits and challenges

OCR offers a number of benefits, but there are also some challenges that can come with it. 

OCR advantagesOCR challenges
Save time: OCR enables automatic capture and processing of text, eliminating the need for manual input and transcription. This saves a lot of time.Image quality: OCR is sensitive to image quality. Poor scan or image capture quality can affect the accuracy of text recognition.
Increased efficiency: Automatic processing of documents and extraction of information can speed up workflows and increase efficiency.Fonts and handwriting: Different fonts, unclear or illegible writing, and handwriting pose challenges to OCR accuracy.
Minimize human error: OCR reduces the likelihood of human error when capturing or transmitting data because text recognition is automatic.Multilingualism: Recognizing text in different languages can be a challenge, as each language has its own peculiarities and fonts.
Accessibility: OCR enables the conversion of printed or handwritten text into machine-readable formats, making it easier for people with visual impairments to access information.Accuracy: Although OCR systems are becoming increasingly accurate, there is still some margin for error in text recognition. Especially with complex documents or poor quality, accuracy can be compromised.
Document searchability: OCR enables text in digital documents to be searched. This allows relevant information to be found and extracted quickly.Formatting and structuring: Correctly recognizing formatting elements such as tables, columns, font sizes, or text alignments can be a challenge.

Depending on the specific OCR system and the algorithms and techniques used, how OCR works varies. Advances in image processing, machine learning, and artificial intelligence have led to continuous improvements in OCR accuracy and performance.

Use cases - OCR in the application

There is a wide range of applications for OCR (Optical Character Recognition). 

Here are some examples:

Document processing

OCR enables automatic capture and processing of printed documents such as invoices, contracts, forms, reports, and more. 

The text can be extracted, searched and imported into digital systems.

Digital archiving

OCR allows printed documents to be converted into digital formats and stored in electronic archives. 

This facilitates the search, access and management of documents.

Automatic data entry

OCR enables automatic extraction of data from printed forms or tables. 

Information such as name, address, phone number, item numbers, etc. can be captured and used in databases or CRM systems.

Text extraction from images

OCR can be used to extract text from images or photos. 

This is helpful when text in images needs to be made available or searchable.

Number recognition

OCR can be used to recognize and extract numbers, such as in automatic license plate recognition (ANPR) for traffic monitoring or in the processing of banking and financial documents.

Translation and language processing

OCR can be used as a precursor to machine translation. 

Recognized text can be automatically translated into other languages or used for language-based analysis and processing.


By converting printed text into digital formats, OCR assists people with visual impairments or reading difficulties by converting text into speech output or Braille systems.

Mail and parcel processing

OCR is used in logistics to recognize addresses on letters or parcels and optimize delivery. Often, however, machine-readable information can already be encoded as a barcode and be processed via Barcode scanner instead of OCR technology.

These application areas are just a few examples, and OCR is used in many other areas where automatic text recognition and processing is required.

Will OCR software still exist in the future or will it be completely replaced by AI? 

After our in-depth review of the research you provided, a number of key findings can be derived that have important implications for the future of traditional OCR software.

Importance of post-OCR processing

The study by Nguyen et al. (2021) emphasizes the need for post-OCR processing to increase the accuracy and quality of OCR results. While modern OCR systems provide adequate performance in recognizing modern texts, research shows that their efficiency is significantly reduced when processing historical materials or texts processed with outdated digitization techniques. 

Implementing advanced post-OCR processing techniques based on AI and machine learning could help ameliorate these challenges and expand or replace the role of traditional OCR software.

Influence of OCR errors on text recognition

Hamdi et al. (2022) provide a valuable contribution to the discussion by analyzing the impact of OCR errors on document accessibility and specific Natural Language Processing tasks, such as named entity recognition and linking. 

Despite the significant improvements in OCR technology, their research highlights the need to implement advanced error handling and post-OCR correction strategies to achieve reliable results.

Customized OCR solutions

Jain et al. (2023) point out that creating OCR solutions with human-like capabilities, especially when processing handwritten text or text with unique writing styles, remains a significant challenge. As a solution approach, they suggest the development of adaptive OCR models and personalized solutions that could improve the accuracy of text recognition for these specific use cases by training with specific, individualized data sets.

Overall, these studies indicate that traditional OCR software will likely continue to play an important role, but will increasingly be complemented and enhanced by more advanced technologies and approaches. In particular, the implementation of AI and machine learning, the improvement of post-OCR processing and correction methods, and the development of individualized OCR solutions may contribute to this. As a result, OCR software can be expected to continue to be able to handle a variety of text types and styles, and to do so with increasing accuracy and efficiency.


Hamdi, A., Pontes, E. L., Sidere, N., Coustaty, M., & Doucet, A. (2022). In-depth analysis of the impact of OCR errors on named entity recognition and linking. Cambridge University Press.

Jain, P. H., Kumar, V., Samuel, J., Singh, S., Mannepalli, A., & Anderson, R. (2023). Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods. Information, 14(6), 305.

Nguyen, T. T. H., Jatowt, A., Coustaty, M., & Doucet, A. (2021). Survey of post-OCR processing approaches. L3i, University of La Rochelle.)

Tips for optimizing OCR results

To get the most out of your OCR results, keep the following tips in mind:

  • Careful image capture: Make sure the image quality of the scanned document or photo is high. Use an appropriate resolution and make sure the text is clear and legible.
  • Preprocessing of the image: Clean up the image before OCR processing by removing noise, optimizing brightness and contrast, and correcting any distortion. This will improve readability and OCR results. For image processing, Tesseract uses e.g. Leptonica. The Python OpenCV module offers another free alternative.
  • Adjust the settings of the OCR software: Check the settings of the OCR software you are using. Depending on the text type, font size, or language, adjustments to parameters such as text recognition methods or confidence thresholds can improve the accuracy of the results. Various free technologies can be used here, such as. Tesseract, or commercial software technologies or OCR SaaS offerings and similar technology. 
  • Post-OCR processing: This step enables the recognition and correction of incorrectly recognized text fragments. Although this activity was previously carried out manually, various research results on automation can be used to correct OCR errors.

By applying these tips, you can improve the quality and accuracy of OCR results, increasing the efficiency and reliability of your OCR processing.

Conclusion - increasing efficiency and reducing errors with OCR

OCR is a powerful technology that enables automatic text recognition and offers a wide range of applications: From document processing to data extraction and accessibility, OCR opens up numerous benefits. 

It improves efficiency, reduces errors, and enables document searchability. 

Nevertheless, there are challenges such as image quality or different fonts. Optimizing image capture, pre-processing, and adjusting OCR parameters can increase accuracy in this regard. Machine learning and AI contribute to the continuous development of OCR. 

Integrating OCR with platforms like Konfuzio enables automated data processing, improved data quality, and easier access to information. 

As OCR continues to evolve, the future holds great promise in helping businesses and organizations streamline their workflows and leverage their data more effectively.

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