Unreadable: Challenges and Critical Pedagogy to Optical Character Recognition Software

Posted on April 14, 2023 by Ryan Yoakum

In the 21^st century, Optical Character Recognition (OCR) software has fundamentally changed how we search for information. OCR is the process of taking images with text and making them searchable. The implications of OCR vary from allowing searchability on massive databases to promoting accessibility by making screen readers a possibility. While this is all incredibly helpful, it is not without fault, as there are still many challenges to the OCR process that create barriers for certain projects. There are also some natural limitations to using this software that especially have consequences for time-sensitive projects, but other factors within human control have negatively influenced the development of OCR technology in general. This blog post will explore two issues: the amount of human labor required on an OCR project and the Western biases of this kind of software.

Some text in ABBYY FineReader. Not all of the appropriate text is contained within a box, indicating the human labor that needs to go in to correct this. — _{Public Domain Image}

Human Labor Requirements

While OCR can save an incredible amount of time, it is not a completely automated system. For printed documents from the 20^th-21^st century, most programs can guarantee a 95-99% accuracy rate. The same is not true, however, for older documents. OCR software works by recognizing pre-built characters the software was initially programmed to recognize. When a document does not follow that same pattern, the software cannot recognize it. Handwritten documents are a good example of this, in which the same letter may appear differently to the software, depending on how it was written. Some programs, such as ABBYY FineReader, have attempted to resolve this problem by incorporating a training program, which allows users to train the system to read specific types of handwriting. Even still, that training process requires human input, and there is still much work for individuals to put into ensuring that the processed document is accurate. As a result, OCR can be a time-consuming process that still requires plenty of human labor for a project.

Western Biases

Another key issue with the OCR process is the Western biases that went into the creation of the software. Many common OCR programs were designed to handle projects with Latinized scripts. While helpful for some projects, this left barriers to documents with non-Latinized scripts, particularly from languages commonly used outside the West. While advances have been made on this front, the advancements are still far behind that of Latinized scripts. For example, ABBYY FineReader is one of the few software programs that will scan in non-western languages, but it cannot incorporate its training program when those scripts aren’t Latinized. Adobe Acrobat can also scan documents with languages that use non-Latinized scripts, but its precision is less consistent than with those languages that do.

An old version of ABBYY FineReader. The text scanned on the left is a language with a non-Latinized script. The right side shows a variety of errors due to the system's lack of knowledge of that language. — _{Photo Credit: Paul Tafford}

Addressing the Issues with OCR

Although OCR has performed many amazing tasks, there is still much development needed when it comes to projects related to this aspect of scholarly research. One crucial component when considering taking on an OCR project is to recognize the limitations of the software and to account for that when determining the scope of your project. At this stage, OCR technology is certainly a time-saver and fundamentally changing the possibilities of scholarship, but without human input, these projects fail to make an impact. Likewise, recognizing the inequality of processing for non-western languages in some of the more prevalent OCR software (which several developers have looked to offset by creating OCR programs specifically catered to specific non-Latinized languages). Acknowledging these issues can help us consider the scope of various projects and also allow us to address these issues to make OCR a more accessible field.

Free, Open Source Optical Character Recognition with gImageReader

Posted on November 5, 2020 by Ben Ostermeier

Optical Character Recognition (OCR) is a powerful tool to transform scanned, static images of text into machine-readable data, making it possible to search, edit, and analyze text. If you’re using OCR, chances are you’re working with either ABBYY FineReader or Adobe Acrobat Pro. However, both ABBYY and Acrobat are propriety software with a steep price tag, and while they are both available in the Scholarly Commons, you may want to perform OCR beyond your time at the University of Illinois.

Thankfully, there’s a free, open source alternative for OCR: Tesseract. By itself, Tesseract only works through the command line, which creates a steep learning curve for those unaccustomed to working with a command-line interface (CLI). Additionally, it is fairly difficult to transform a jpg into a searchable PDF with Tesseract.

Thankfully, there are many free, open source programs that provide Tesseract with a graphical user interface (GUI), which not only makes Tesseract much easier to use, some of them come with layout editors that make it possible to create searchable PDFs. You can see the full list of programs on this page.

The program logo for gImageReader

In this post, I will focus on one of these programs, gImageReader, but as you can see on that page, there are many options available on multiple operating systems. I tried all of the Windows-compatible programs and decided that gImageReader was the closest to what I was looking for, a free alternative to ABBYY FineReader that does a pretty good job of letting you correct OCR mistakes and exporting to a searchable PDF.

Installation

gImageReader is available for Windows and Linux. Though they do not include a Mac compatible version in the list of releases, it may be possible to get it to work if you use a package manager for Mac such as Homebrew. I have not tested this though, so I do not make any guarantees about how possible it is to get a working version of gImageReader on Mac.

To install gImageReader on Windows, go to the releases page on Windows. From there, go to the most recent release of the program at the top and click Assets to expand the list of files included with the release. Then select the file that has the .exe extension to download it. You can then run that file to install the program.

Manual

The installation of gImageReader comes with a manual as an HTML file that can be opened by any browser. As of the date of this post, the Fossies software archive is hosting the manual on its website.

Setting OCR Mode

gImageReader has two OCR modes: “Plain Text” and “hOCR, PDF”. Plain Text is the default mode and only recognizes the text itself without any formatting or layout detection. You can export this to a text file or copy and paste it into another program. This may be useful in some cases, but if you want to export a searchable PDF, you will need to use hOCR, PDF mode. hOCR is a standard for formatting OCR text using either XML or HTML and includes layout information, font, OCR result confidence, and other formatting information.

To set the recognition to hOCR, PDF mode, go to the toolbar at the top. It includes a section for “OCR mode” with a dropdown menu. From there, click the dropdown and select hOCR, PDF:

This is the toolbar for gImageReader. You can set OCR mode by using the dropdown that is the third option from the right.

Adding Images, Performing Recognition, and Setting Language

If you have images already scanned, you can add them to be recognized by clicking the Add Images button on the left panel, which looks like a folder. You can then select multiple images if you want to create a multipage PDF. You can always add more images later by clicking that folder button again.

On that left panel, you can also click the Acquire tab button, which allows you to get images directly from a scanner, if the computer you’re using has a scanner connected.

Once you have the images you want, click the Recognize button to recognize the text on the page. Please note that if you have multiple images added, you’ll need to click this button for every page.

If you want to perform recognition on a language other than English, click the arrow next to Recognize. You’ll need to have that language installed, but you can install additional languages by clicking “Manage Languages” in the dropdown appears. If the language is already installed, you can go to the first option listed in the dropdown to select a different language.

Viewing the OCR Result

In this example, I will be performing OCR on this letter by Franklin D. Roosevelt:

Raw scanned image of a typewritten letter signed by Franklin Roosevelt

This 1928 letter from Franklin D. Roosevelt to D. H. Mudge Sr. is courtesy of Madison Historical: The Online Encyclopedia and Digital Archive for Madison County Illinois. https://madison-historical.siue.edu/archive/items/show/819

Once you’ve performed OCR, there will be an output panel on the right. There are a series of buttons above the result. Click the button on the far right to view the text result overlaid on top of the image:

The text result of performing OCR on the FDR letter overlaid on the original scan.

Here is the the text overlaid on an image of the original scan. Note how the scan is slightly transparent now to make the text easier to read.

Correcting OCR

The OCR process did a pretty good job with this example, but it there are a handful of errors. You can click on any of the words of text to show them on the right panel. I will click on the “eclnowledgment” at the end of the letter to correct it. It will then jump to that part of the hOCR “tree” on the right:

hOCR tree in gImageReader, which shows the recognition result of each word in a tree-like structure.

The hOCR tree in gImageReader, which also shows OCR result.

Note in this screenshot I have clicked the second button from the right to show the confidence values, where the higher the number, the higher the confidence Tesseract has with the result. In this case, it is 67% sure that eclnowledgement is correct. Since it obviously isn’t correct, we can type new text by double-clicking on the word in this panel and type “acknowledgement.” You can do this for any errors on the page.

Other correction tips:

If there are any regions that are not text that it is still recognizing, you can right click them on the right and delete them.
You can change the recognized font and its size by going to the bottom area labeled “Properties.” Font size is controlled by the x_fsize field, and x_font has a dropdown where you can select a font.
It is also possible to change the area of the blue word box once it is selected, simply by clicking and dragging the edges and corners.
If there is an area of text that was not captured by the recognition, you can also right click in the hOCR “tree” to add text blocks, paragraphs, textlines, and words to the document. This allows you to draw a box on image and then type what the text says.

Exporting to PDF

Once you are done making OCR corrections, you can export to a searchable PDF. To do so, click the Export button above the hOCR “tree,” which is the third button from the left. Then, select export to PDF. It then gives you several options to set the compression and quality of the PDF image, and once you click OK, it should export the PDF.

Conclusion

Unfortunately, there are some limitations to gImageViewer, as can often be the case with free, open source software. Here are some potential problems you may have with this program:

While you can add new areas to recognize with OCR, there is not a way to change the order of these elements inside the hOCR “tree,” which could be an issue if you are trying to make the reading order clear for accessibility reasons. One potential workaround could be to use the Reading Order options on Adobe Acrobat, which you can read about in this libguide.
You cannot show the areas of the document that are in a recognition box unless you click on a word, unlike ABBYY FineReader which shows all recognition areas at once on the original image.
You cannot perform recognition on all pages at once. You have to click the recognition button individually for each page.
Though there are some image correction options to improve OCR, such as brightness, contrast, and rotation, it does not have as many options as ABBYY FineReader.

gImageViewer is not nearly as user friendly or have all of the features that ABBYY FineReader has, so you will probably want to use ABBYY if it is available to you. However, I find gImageViewer a pretty good program that can meet most general OCR needs.

Choosing an OCR Software: ABBYY FineReader vs. Adobe Acrobat Pro

Posted on March 5, 2020 by Mallory E. Untch

What is OCR? OCR stands for Optical Character Recognition. This is the electronic identification and digital encoding of typed or printed text by means of an optical scanner or a specialized software. Performing OCR allows computers to read static images of text to convert them to readable, editable, and searchable data on a page. There are many applications of OCR including the creation of more accessible documents for the blind and visually-impaired, text/data mining projects, textual comparisons, and large-scale digitization projects.

There are a different software options to consider when you are performing OCR on you documents and it can be challenging to understand which one is best for you. So let’s break it down. Continue reading →

Cool Text Data – Music, Law, and News!

Posted on December 5, 2018 by Kayla Abner

Computational text analysis can be done in virtually any field, from biology to literature. You may use topic modeling to determine which areas are the most heavily researched in your field, or attempt to determine the author of an orphan work. Where can you find text to analyze? So many places! Read on for sources to find unique text content.

Genius – the song lyrics database

Genius started as Rap Genius, a site where rap fans could gather to annotate and analyze rap lyrics. It expanded to include other genres in 2014, and now manages a massive database covering Ariana Grande to Fleetwood Mac, and includes both lyrics and fan-submitted annotations. All of this text can be downloaded and analyzed using the Genius API. Using Genius and a text mining method, you could see how themes present in popular music changed over recent years, or understand a particular artist’s creative process.

Homepage of case.law

Case.law – the case law database

The Caselaw Access Project (CAP) is a fairly recent project that is still ongoing, and publishes machine-readable text digitized from over 40,000 bound volumes of case law from the Harvard Law School Library. The earliest case is from 1658, with the most recent cases from June 2018. An API and bulk data downloads make it easy to get this text data. What can you do with huge amounts of case law? Well, for starters, you can generate a unique case law limerick:

Wheeler, and Martin McCoy.
Plaintiff moved to Illinois.
A drug represents.
Pretrial events.
Rocky was just the decoy.

Check out the rest of their gallery for more project ideas.

Newspapers and More

There are many places you can get text from digitized newspapers, both recent and historical. Some newspaper are hundreds of years old, so there can be problems with the OCR (Optical Character Recognition) that will make it difficult to get accurate results from your text analysis. Making newspaper text machine readable requires special attention, since they are printed on thin paper and have possibly been stacked up in a dusty closet for 60 years! See OCR considerations here, but the newspaper text described here is already machine-readable and ready for text mining. However, with any text mining project, you must pay close attention to the quality of your text.

The Chronicling America project sponsored by the Library of Congress contains digital copies of newspapers with machine-readable text from all over the United States and its territories, from 1690 to today. Using newspaper text data, you can analyze how topics discussed in newspapers change over time, among other things.

Looking for newspapers from a different region? The library has contracts with several vendors to conduct text mining, including Gale and ProQuest. Both provide newspaper text suitable for text mining, from The Daily Mail of London (Gale), to the Chinese Newspapers Collection (ProQuest). The way you access the text data itself will differ between the two vendors, and the library will certainly help you navigate the collections. See the Finding Text Data library guide for more information.

The sources mentioned above are just highlights of our text data collection! The Illinois community has access to a huge amount of text, including newspapers and primary sources, but also research articles and books! Check out the Finding Text Data library guide for a more complete list of sources. And, when you’re ready to start your text mining project, contact the Scholarly Commons (sc@library.illinois.edu), and let us help you get started!

Lightning Review: Optical Character Recognition: An Illustrated Guide to the Frontier

Posted on September 19, 2018 by William Tringali

Lightning Review: Optical Character Recognition: An Illustrated Guide to the Frontier

Stephen V. Rice, George Nagy, and Thomas A. Nartaker’s work on OCR, though written in 1999, is still a remarkably valuable bedrock text for diving into the technology. Though OCR systems have, and continue to, evolve with each passing day, the study presented within their book still highlights some of the major issues one faces when performing optical character recognition. Text is in an unusual typeface or contains stray marks, print is too heavy or too light. This text gives those interested in learning the general problems that arise in OCR a great guide to what they and their patrons might encounter.

The book opens with a quote from C-3PO, and a discussion of how our collective sci-fi imagination believe technology will have “cognitive and linguistic abilities” that match and perhaps even exceed our own (Rice et al., 1999, p. 1).

The human eye is the most powerful character identifier to exist. As the authors note “A seven year old child can identify characters with far greater accuracy than the leading OCR systems” (Rice et al., 1999, 165). I found this simple explanation so helpful for when I get questions here in the Scholarly Commons from patron who are confused as to why their document, even after been run through and OCR software, is not perfectly recognized. It is very easy, with our human eyes, to discern when a mark on a page is nothing of importance, and when it is a letter. Ninety-nine percent character accuracy doesn’t mean ninety-nine percent page accuracy.

In summary, this work presents a great starting point for those with an interest in understanding OCR technology, even at almost two decades old.

Give it, and the many other fabulous books in our reference collection, a read!

What To Do When OCR Software Doesn’t Seem To Be Working

Posted on July 19, 2017 by C. Berman

Optical character recognition can enhance your research!

While optical character recognition (OCR) is a powerful tool, it’s not a perfect one. Inputting a document into an OCR software doesn’t necessarily mean that the software will actually output something useful 100% of the time. Though most documents come out without a hitch, we have a few tips on what to do if your document just isn’t coming out.

Scanning Issues

The problem may be less with your program and more with your initial scan. Low-quality scans are less likely to be read by OCR software. Here are a few considerations to keep in mind when scanning a document you will be using OCR on:

Make sure your document is scanned at 300 DPI
Keep your brightness level at 50%
Try to keep your scan as straight as possible

If you’re working with a document that you cannot create another scan for, there’s still hope! OCR engines with a GUI tend to have photo editing tools in them. If your OCR software doesn’t have those tools, or if their provided tools aren’t cutting it, try using a photo manipulation tool such as Photoshop or GIMP to edit your document. Also, remember OCR software tends to be less effective when used on photographs than on scans.

Textual Issues

The issues you’re having may not stem from the scanning, but from the text itself. These issues can be more difficult to solve, because you cannot change the content of the original document, but they’re still good tips to know, especially when diagnosing issues with OCR.

Make sure that your document is in a language, and from a period that your OCR software recognizes; not all engines are trained to recognize all languages
Low contrast in documents can reduce OCR accuracy; contrast can be adjusted in a photo manipulation tool
Text created prior to 1850 or with a typewriter can be more difficult for OCR software to read
OCR software cannot read handwriting; while we’d all like to digitize our handwritten notes, OCR software just isn’t there yet

Working with Digital Files

Digital files can, in many ways, be more complicated to use OCR software on, just because someone else may have made the file. This means that a file is lower-quality to begin with, or that whoever scanned the file may have made errors. Most likely, you will run into scenarios that are easy fixes using photo manipulation tools. But there will be times that the images you come across just won’t work. It’s frustrating, but you’re not alone. Check out your options!

Always Remember that OCR is Imperfect

Even with perfect documents that you think will yield perfect results, there will be a certain percentage of mistakes. Most OCR software packages have an error rate between 97-99% per character. While this may seem like it’s not many errors, in a page with 1,800 characters, there will be between 18 and 54 errors. In a 300 page book with 1,800 characters per page, that’s between 5,400 and 16,200. So always be diligent and clean up your OCR!

The Scholarly Commons

Here at the Scholarly Commons, we have Adobe Acrobat Pro installed on every computer, and ABBYY FineReader installed on several. We can also help you set up Tesseract on your own computer. If you would like to learn more about OCR, check out our LibGuide and keep your eye open for our next Making Scanned Text Machine Readable through Optical Character Recognition Savvy Researcher workshop!

Learning to Make Documents Accessible with OCR Software

Posted on December 20, 2016 by C. Berman

Photo via pexels.com.

Accessibility in the digital age can be difficult for people to understand, especially given the sheer amount of ways to present information on the computer. However, creating content that is accessible to all individuals should be a priority for researchers. Creating accessible documents is an easy process, and the Scholarly Commons has the software you need to make that happen.

Optical character recognition software (otherwise known as OCR) has the ability to convert scanned documents, PDF documents, and image documents into editable and searchable documents. Documents that have gone through OCR software can then be recognized by, and read through screen reader software. Screen readers are tools oftentimes used by those with visual impairments; they convert textual content into ‘synthesized’ speech, which is then read aloud to the user.

One trick to see whether or not a digital document is accessible is to try to highlight a line of text and then copy-paste it into another document. If you can successfully do that, your document is ready to be read by a screen reader. If you cannot highlight a single line of text and/or copy-paste it, you may want to consider putting your document through OCR software. However, if you have a “protected” PDF, you will not be able to reformat the document for accessibility.

OCR readers can read more than just digital documents – they are powerful tools that can also perform their function on scanned documents, either typed or handwritten. That is not to say that they are infallible, however. OCR software may have difficulties reading documents created before 1850, and may not always be 100% accurate. The user must be vigilant to make sure that mistakes don’t creep their way into the final product.

The Scholarly Commons is outfitted with two OCR programs: ABBY FineReader, and Adobe Acrobat. To read more on the specifics of each software, see the ABBY FineReader LibGuide or Adobe Acrobat’s Guide to OCR. There are also numerous options online for PDF readers online — look around and find the option that works best for you. Just a little time with this user-friendly software can make not only your research accessible, but to make the world a little more accessible as a whole.

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