Simple NetInt: A New Data Visualization Tool from Illinois Assistant Professor, Juan Salamanca

Posted on March 11, 2021 by Mallory E. Untch

Juan Salamanca Ph.D, Assistant Professor in the School of Art and Design at the University of Illinois Urbana-Champaign recently created a new data visualization tool called Simple NetInt. Though developed from a tool he created a few years ago, this tool brings entirely new opportunities to digital scholarship! This week we had the chance to talk to Juan about this new tool in data visualization. Here’s what he said…

Simple NetInt is a JavaScript version of NetInt, a Java-based node-link visualization prototype designed to support the visual discovery of patterns across large dataset by displaying disjoint clusters of vertices that could be filtered, zoomed in or drilled down interactively. The visualization strategy used in Simple NetInt is to place clustered nodes in independent 3D spaces and draw links between nodes across multiple spaces. The result is a simple graphic user interface that enables visual depth as an intuitive dimension for data exploration.

Check out the Simple NetInt tool here!

In collaboration with Professor Eric Benson, Salamanca tested a prototype of Simple NetInt with a dataset about academic publications, episodes, and story locations of the Sci-Fi TV series Firefly. The tool shows a network of research relationships between these three sets of entities similar to a citation map but on a timeline following the episodes chronology.

What inspired you to create this new tool?

This tool is an extension of a prototype I built five years ago for the visualization of financial transactions between bank clients. It is a software to visualize networks based on the representation of entities and their relationships and nodes and edges. This new version is used for the visualization of a totally different dataset: scholarly work published in papers, episodes of a TV Series, and the narrative of the series itself. So, the network representation portrays relationships between journal articles, episode scripts, and fictional characters. I am also using it to design a large mural for the Siebel Center for Design.

What are your hopes for the future use of this project?

The final goal of this project is to develop an augmented reality visualization of networks to be used in the field of digital humanities. This proof of concept shows that scholars in the humanities come across datasets with different dimensional systems that might not be compatible across them. For instance, a timeline of scholarly publications may encompass 10 or 15 years, but the content of what is been discussed in that body of work may encompass centuries of history. Therefore, these two different temporal dimensions need to be represented in such a way that helps scholars in their interpretations. I believe that an immersive visualization may drive new questions for researchers or convey new findings to the public.

What were the major challenges that came with creating this tool?

The major challenge was to find a way to represent three different systems of coordinates in the same space. The tool has a universal space that contains relative subspaces for each dataset loaded. So, the nodes instantiated from each dataset are positioned in their own coordinate system, which could be a timeline, a position relative to a map, or just clusters by proximities. But the edges that connect nodes jump from one coordinate system to the other. This creates the idea of a system of nested spaces that works well with few subspaces, but I am still figuring out what is the most intuitive way to navigate larger multidimensional spaces.

What are your own research interests and how does this project support those?

My research focuses on understanding how designed artifacts affect the viscosity of social action. What I do is to investigate how the design of artifacts facilitates or hinders the cooperation of collaboration between people. I use visual analytics methods to conduct my research so the analysis of networks is an essential tool. I have built several custom-made tools for the observation of the interaction between people and things, and this is one of them.

If you would like to learn more about Simple NetInt you can find contact information for Professor Juan Salamanca here and more information on his research!

If you’re interested in learning more about data visualizations for your own projects, check out our guide on visualizing your data, attend a Savvy Researcher Workshop, Live Chat with us on Ask a Librarian, or send us an email. We are always happy to help!

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.

Meet Spencer Keralis, Digital Humanities Librarian

Posted on September 19, 2019 by Abigail Sewall

This latest installment of our series of interviews with Scholarly Commons experts and affiliates features one of the newest members of our team, Spencer Keralis, Digital Humanities Librarian.

What is your background and work experience?

I have a Ph.D. in English and American Literature from New York University. I started working in libraries in 2011 as a Council on Library and Information Resources (CLIR) Fellow with the University of North Texas Libraries, doing research on data management policy and practice. This turned into a position as a Research Associate Professor working to catalyze digital scholarship on campus, which led to the development of Digital Frontiers, which is now an independent non-profit corporation. I serve as the Executive Director of the organization and help organize the annual conference. I have previous experience working as a project manager in telecom and non-profits. I’ve also taught in English and Communications at the university level since 2006.

What led you to this field?

My CLIR Fellowship really sparked the career change from English to libraries, but I had been considering libraries as an alternate career path prior to that. My doctoral research was heavily archives-based, and I initially thought I’d pursue something in rare books or special collections. My interest in digital scholarship evolved later.

What is your research agenda?

My current project explores how the HIV-positive body is reproduced and represented in ephemera and popular culture in the visual culture of the early years of the AIDS epidemic. In American popular culture, representations of the HIV-positive body have largely been defined by Therese Frare’s iconic 1990 photograph of gay activist David Kirby on his deathbed in an Ohio hospital, which was later used for a United Colors of Benetton ad. Against this image, and other representations which medicalized or stigmatized HIV-positive people, people living with AIDS and their allies worked to remediate the HIV-positive body in ephemera including safe sex pamphlets, zines, comics, and propaganda. In my most recent work, I’m considering the reclamation of the erotic body in zines and comics, and how the HIV-positive body is imagined as an object of desire differently in these underground publications than they are in mainstream queer comics representing safer sex. I also consider the preservation and digitization of zines and other ephemera as a form of remediation that requires a specific ethical positioning in relation to these materials and the community that produced them, engaging with the Zine Librarians’ Code of Conduct, folksonomies and other metadata schema, and collection and digitization policies regarding zines from major research libraries. This research feels very timely and urgent given rising rates of new infection among young people, but it’s also really fun because the materials are so eclectic and often provocative. You can check out a bit of this research on the UNT Comics Studies blog.

Do you have any favorite work-related duties?

I love working with students and helping them develop their research questions. Too often students (and sometimes faculty, let’s be honest) come to me and ask “What tools should I learn?” I always respond by asking them what their research question is. Not every research question is going to be amenable to digital tools, and not every tool works for every research question. But having a conversation about how digital methods can potentially enrich a student’s research is always rewarding, and I always learn so much from these conversations.

What are some of your favorite underutilized resources that you would recommend to researchers?

I think comics and graphic novels are generally underappreciated in both pedagogy and research. There are comics on every topic, and historical comics go back much further than most people realize. I think the intersection of digital scholarship with comics studies has a lot of potential, and a lot of challenges that have yet to be met – the technical challenge of working with images is significant, and there has yet to be significant progress on what digital scholarship in comics might look like. I also think comics belong more in classes – all sorts of classes, there are comics on every topic, from math and physics, to art and literature – than they are now because they reach students differently than other kinds of texts.

If you could recommend one book or resource to beginning researchers in your field, what would you recommend?

I’m kind of obsessed with Liz Losh and Jacque Wernimont’s edited collection Bodies of Information: Intersectional Feminism and Digital Humanities because it’s such an important intervention in the field. I’d rather someone new to DH start there than with some earlier, canonical works because it foregrounds alternative perspectives and methodologies without centering a white, male perspective. Better, I think, to start from the margins and trouble some of the traditional narratives in the discipline right out the gate. I’m way more interested in disrupting monolithic or hegemonic approaches to DH than I am in gatekeeping, and Liz and Jacque’s collection does a great job of constructively disrupting the field.

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