Blog Layout

Optical Character Recognition (OCR). What Is OCR, Its Benefits, limitations, and AI

Michael Meteling • October 31, 2023

Optical Character Recognition is the technology used to convert images of handwritten or printed text into a digital machine-readable text, this way the text can be edited, manipulated, or processed electronically. Through its system, OCR software allows you to extract written data from a scanned document or image without having to manually enter it. 



With this technology, you can turn the text in a PDF or even a phone-taken picture into an editable document in just a couple of seconds or maybe minutes, depending on the size of your document. 



What does OCR stand for and how does it work

The software follows a stepped process to be able to recognize the characters. 


Starting with pre-processing: here the software basically “cleans” the document of non-glyph (a character image) elements and any other spot in the document. Also, it smoothes the edges, “tides up” any details like proper alignment of the text, and separates the background from the text. 


Later, the software’s algorithm starts recognizing the text through one of two methods: pattern recognition or feature extraction. The first takes each image or glyph (the letters or characters) as a whole and compares it with glyphs already stored in the software. Normally this method works better with neat fonts that can be easily recognized. The second method analyzes the parts of the glyph like its curves, lines, angles, and everything separately, to identify the letter. This method works with any kind of font, printed or handwritten, cursive, or script. 


Source: How Does Optical Character Recognition (OCR) Work? - YouTube, 0:37

Lastly comes the post-processing. This corrects any errors there might be in the detected text by comparing the words with the vocabulary stored in the software, which could be normal vocabulary or more technical/specialized in a specific field. 


Afterward, the text is ready to use. Some OCR software includes features like the creation of a file with the format of your choice, like for instance a PDF document, with the extracted text. 


The OCR technology can be applied both through hardware and software or software only. The hardware part mainly plays the role of a scanner and the software processes the information. 

OCR benefits


OCR recognition in pictures

A lot of this article has talked about OCR from a business perspective and for a business use case. However, OCR could also be used by consumers, for example by consumers taking pictures of something which they can then get translated or read aloud for them. Additionally, it could even help businesses, for example when an employee travels and has to upload bills for approval, a picture can be taken, the data can be extracted and enrolled into logic workflows in an automatic approval process.




Source: CLOVA OCR - AI Services - NAVER Cloud Platform (ncloud.com)


AI OCR - Artificial Intelligence


Recently AI has become more and more popular and AI applications more prevalent. So how would AI work with OCR? There are several potential applications of AI with OCR software. It could be used to enhance or potentially even replace traditional OCR software and use machine learning algorithms to extract and convert the document into editable text.


AI as an add-on for OCR


A second application would be as an add-on to OCR software, OCR can be used to read the file and convert it into an electronic document. AI can then be used to read and analyze the document. This could be helpful if you want to automatically classify the document for example or extract a certain part of text. Additionally, AI could be used to check the electronic document and flag and potentially even attempt to fix mistakes that occurred during the transition to a digital version.



Natural language processing and OCR

NLP or natural language processing is a method through which machine learning is utilized to try and give a computer the ability to understand, interpret, and comprehend language. This is very useful since OCR requires the characters to be matched to words and vocabulary, but the next step in that process would be for OCR software to understand the meaning of language. 


If that would be the case then the software can make decisions on what is correct and what is not correct and needs to be corrected or omitted. This is especially interesting in the case of lower accuracy OCR or handwritten texts since it can fix grammar mistakes and still output a comprehensive text without potentially knowing all the characters.

Source: How Does Optical Character Recognition (OCR) Work? - YouTube, 0:58

What are the limitations of OCR

It requires additional software

One of the main limitations of OCR is that it will most likely require additional software or machine learning algorithms to provide the desired output. The basics of OCR try to extract a character, the result is a string of unrelated characters. As also mentioned above, vocabulary is then needed to form meaningful words and sentences instead of just characters. How successful that is depends on how good the vocabulary software is.

OCR accuracy

Accuracy is also still a potential issue with a lot of OCR software, for printed text the accuracy can be around 98-99%, which means that in a 1000-character document, 10-20 characters might not be correct. This limitation can be partially solved by using better vocabulary software and potentially machine learning, to be able to still output correct words and sentences. However, this does not include handwritten texts which could lower the accuracy by a substantial amount depending on the handwriting.



Factors that can influence OCR accuracy in computer documents



Quality of the print

If the document is printed in a lower quality this could severely impair OCR capabilities. This includes when the text can get blurry, the text is smudged or the ink is of insufficient quality or quantity when printing. The DPI selected while printing can also have a significant impact, depending on the letter size, color, and how close together the text is, if the DPI of the page is low it could negatively impact OCR.

Quality of the scan

If a document is scanned or a picture is taken the quality of said scan can influence how easy it is for OCR software to extract characters. For example, if the image of the scan gets glary, this can make it difficult to read. Also here DPI can affect the quality, depending on what DPI is chosen for the scan quality. Lastly, if the document is not aligned properly or potentially skewed, character identification can get more tricky.

OCR Text limitations

There is a large variety of letters from different alphabets, and certain alphabets are easier to recognize than others. For example in Arabic even printed letters could be printed in cursive which could make it harder to recognize for OCR, this could also include cursive with any alphabet. Additionally, there are many different variations of fonts and font sizes, with some drastically changing how the characters look. Couples, fonts, sizes, and layouts with certain letters or even numbers that look a lot like each other, like the zero and the letter o, and this can cause problems for OCR technology.

Limitations of OCR with handwritten text

It is clear that OCR can already have problems with scanned computer-generated texts, due to several factors that can make it difficult to detect. In the case of handwriting, these factors are multiplied, since there are almost exponential differences in how someone might write. Depending on what alphabet they use, how they were taught, and more. Additionally, human-written texts are of course a lot more error-prone than computer texts that have spell or grammar checks. This can add an extra problem to OCR at the stage where the characters are trying to be matched to vocabulary.

JetStream AI - the future beyond OCR Software

Standard OCR software needs bitonal images, this means that each pixel in the image file will be interpreted as either black or white and that tonal value is then stored in one bit of digital data). This data is then used to interpret what character it is. Jetstream AI’s abilities on the other hand go beyond the need of bitonal images. Jetstream AI uses deep learning technologies that are baked on neural nets and GPT.


By using AI, Jetstream offers 99%+ accuracy for machine-printed text and over 95% accuracy for handwritten text. Since it is an AI solution it can also learn and adapt over time to the specific needs of the business or industry it is being used in. Jetstream AI has also been trained to better combat the current OCR limitations like interpreting glary, highlighted, or smudged text.


The Jetstream software also offers the ability to search inside of documents, so instead of having to convert a document, which can cause errors or mistakes, and then search it. With Jetstream you can directly search in documents with handwritten text and find instances of that word or phrase. Additionally, it can be easily incorporated into automation workflows to for example classify the documents or extract certain data based on a set of parameters. 


Conclusion

Optical Character Resolution or OCR, is a handy tool that can be used to turn photos or scans into a regular text document that can be interacted with, searched, manipulated, or edited. It does this by recognizing characters in the document and then converting those characters to words and vocabulary. 


However there are currently still a lot of limitations with OCR technology, mainly accuracy problems, these accuracy problems have a wide range of causes from, print quality, scan quality, photo quality, text quality, different fonts, layouts, and more. Handwritten text is more prone to these limitations. There are also limitations with the interpretation of characters into actual words and sentences, especially if the accuracy of the interpretation is already low. 


Artificial intelligence, natural language learning, and machine learning seem to be the main factors that can help OCR technology. Especially on the identification of words and meaning from the characters. If the interpretation of the characters can understand the meaning of the language through AI it can correct mistakes that could have occurred during the OCR scanning. 


Jetstream AI solves many of these issues that OCR technology has, by using AI to overcome a lot of text quality issues to improve the accuracy of the OCR. Additionally, it allows for many more capabilities like training the model for a specific use case and searching handwritten documents for specific keywords or phrases directly in the handwritten document.

Learn More

Zoom in picture of the display of the DeskPro 3x1 desktop scanner.
By Michael Meteling June 26, 2024
What is FADGI The Federal Agencies Digital Guidelines Initiative (FADGI) is a collaborative effort by U.S. federal agencies to establish comprehensive standards for digitizing and preserving historical, archival, and cultural materials. Initiated in 2007, FADGI aims to ensure that digitized content maintains the highest levels of quality, and accessibility, making it a critical framework for institutions involved in digital preservation. The guidelines cover various aspects of digitization, including image capture, metadata embedding, and quality control, ensuring that digital reproductions are accurate and durable. Changes to FADGI in 2024 According to federal mandate M-23-07, all permanent records sent to the National Archives and Records Administration (NARA) after June 30, 2024, will be required to be submitted in an electronic format with a minimum FADGI 3-star rating. The 2024 updates to the FADGI guidelines include several significant enhancements to support this mandate. Key changes include new standards for embedding metadata in WebVTT files and updated guidelines for imaged audio systems. These enhancements are designed to improve the documentation and accessibility of digital records, ensuring that they remain useful and accessible over the long term. Understanding the FADGI 3-Star Rating A 3-star FADGI rating represents a high standard of digitization quality, balancing accuracy and efficiency. To achieve this rating, digitization projects should meet several criteria across multiple evaluation parameters, including spatial resolution, color accuracy, noise reduction, and metadata embedding. These parameters ensure that the digitized materials are of high quality and suitable for a wide range of uses. A 3-star rating is typically required for most professional digitization activities, making it a crucial benchmark for institutions seeking to preserve their digital records effectively. interScan FADGI Compliant Scanners We offer a comprehensive range of scanners that are fully compliant with FADGI standards, ensuring that your digitization projects meet the necessary quality benchmarks. Our product lineup includes: Desktop Scanners : Ideal for small to medium-sized businesses, these scanners offer high-quality digitization in a compact form factor, perfect for offices with limited space. High Volume Scanners : Designed for large-scale digitization projects, these scanners handle high throughput with ease, ensuring quick and efficient processing without compromising on quality. High Capacity Scanners : Suitable for institutions with substantial digitization needs, these scanners are built to manage extensive collections, providing robust performance and durability. Customizable Scanners : Tailored to meet specific requirements, these scanners offer flexible configurations and advanced features to cater to unique digitization challenges. All our scanners are engineered to capture high-resolution images with precise color accuracy, minimal noise, and comprehensive metadata embedding, making them ideal for achieving a FADGI 3-star rating. For more information about our FADGI compliant scanners click here . Leveraging JetStream for FADGI Compliance JetStream is our advanced AI and machine learning software suite specifically designed to enhance digitization processes and ensure FADGI compliance. JetStream comprises three powerful modules: JetStream Recognition , JetStream Classification , and JetStream Extraction . Each module plays a crucial role in optimizing various aspects of digitization that could help to meet FADGI standards. Superior OCR/ICR Results : JetStream Recognition delivers exceptional accuracy in Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR), handling distorted or poor-quality scans, machine-printed text, and hard-to-read handwriting with ease. This ensures that all textual information is accurately captured and preserved, aligning with FADGI’s emphasis on accurate data capture, consistent resolution, and precise metadata embedding. Increased Automation : JetStream automates the digitization workflow, significantly reducing the need for manual intervention. This not only speeds up the digitization process but also minimizes potential human error, ensuring consistent quality across all digitized materials. Rule-Free Few-Shot Learning for Document Categorization : JetStream Classification offers a rule-free, few-shot learning capability for document categorization tasks, significantly expediting the setup and maintenance of workflows compared to traditional methods. This efficiency supports streamlined processes and accurate metadata management, crucial for FADGI compliance. Intelligent Zonal Data Extraction : JetStream Extraction utilizes rule-free, few-shot learning for intelligent zonal data extraction, eliminating error-prone, time-consuming rule-based methods. Users can effortlessly capture individual data fields from documents, drastically speeding up workflow setup and maintenance, ensuring accurate data capture and streamlined processes. Enhanced Metadata Embedding : JetStream ensures that all digitized content includes comprehensive metadata, which is essential for documentation, discoverability, and long-term management of digital archives. Seamless Integration : Designed to integrate easily into existing digitization workflows, JetStream automates tasks such as metadata embedding, quality control checks, and file format conversions, ensuring that all digitized materials could meet FADGI standards. Conclusion FADGI plays a vital role in setting the standards for high-quality digitization and preservation of cultural heritage materials. As the guidelines evolve in 2024, achieving a FADGI 3-star rating becomes increasingly important for institutions involved in digital preservation. Our FADGI-compliant scanners and advanced AI-driven JetStream software provide the tools necessary to meet these standards, ensuring that your digitization projects are efficient, accurate, and fully compliant with FADGI guidelines. By leveraging our solutions, you can enhance your digitization efforts and preserve your valuable records for future generations.
File explorer, that highlights the pictures folder and scans subfolder.
By Michael Meteling December 3, 2023
When you have a physical document that you want in a digital format you will have to scan that document using a scanner, your phone camera, or an app. But what happens when you scan your document? Where will it go and how can you find your document? This article will go over some of the common places where documents can be after they are scanned. Every computer and phone settings can be different which means this might not work in your particular case.
Picture of a Android homescreen with the  files app circled.
By Michael Meteling December 3, 2023
On Android phones, the location where scanned documents are saved can vary based on the scanning app being used and the settings configured within those apps. It can also depend on what brand phone you have since the default app for files could vary slightly.
Iphone with
By Michael Meteling December 3, 2023
Did you just scan a document but can't locate the document on your device? Or just want some more information regarding Apple file management? Then this article might be helpful to you.
More Articles
Share by: