The San Francisco Bay Area’s Response to the AIDS Epidemic: 1 year update on the National Endowment for the Humanities Implementation Grant

Archives and Special Collections has just submitted its annual report to the National Endowment for the Humanities on the collaborative mass digitization grant The San Francisco Bay Area’s Response to the AIDS Epidemic. 

At the one year point of The Bay Area’s Response to the AIDS Epidemic, the consortium of UCSF Library Archives & Special Collections, San Francisco Public Library History Center/Hormel LGBTQIA Center, Gay, Lesbian, Bisexual, Transgender Historical Society, and UC Merced have made significant headway towards our goal of digitizing and publishing 127,000 pages from our various AIDS History collections.

To date we have published seven complete collections on Calisphere, and we have scanned and published the poster component of UCSF’s AIDS History Project Ephemera collection. Thirteen other collections have nearly completed the digitization process and are undergoing quality control checks before being harvested into Calisphere.

The Ultimate Point: Shooting Up and Sharing Needles Puts You at Risk for AIDS. This Fact May Save Your Life!

The Ultimate Point (SF AIDS Foundation). AIDS History Project Ephemera Collection, MSS 2000-31

40,518 pages of materials have to date been uploaded to the Nuxeo Digital Asset Management System we use for managing and publishing to Calisphere.  Some of these have gone to active publication, some are still undergoing quality assurance (QA) procedures. An additional 35,061 pages have been scanned, but have yet to be ingested into the DAMS.

We have also given talks at four library and archives conferences in the past year to share details about our project.

In the coming year we will continue digitizing and publishing collection materials to Calisphere.org and begin planning online exhibits for Calisphere and Digital Public Library of America that will serve to unite and interpret the collections across our partnering institutions.

AIDS Legal Referral Panel's AIDSLaw Conference. Friday November 11, 1988.

AIDSLaw Conference 1988. AIDS Legal Referral Panel Records, 2000-46, Box 2, Folder 8 (GLBTHS)

Digitized collections currently online:

  1. ACT-UP Golden Gate Records, 1988-1993, MSS 98-47 https://calisphere.org/collections/308/ 
  2. Barbara Cameron Papers, 1968-2003 (SFPL GLC 63) https://calisphere.org/collections/27002/
  3. Shanti Project Records, 1982-1994, MSS 98-48 https://calisphere.org/collections/19989/
  4. AIDS Legal Referral Panel (ALRP) records, 1984-2000, (#2000-46) GLBT HS  https://calisphere.org/collections/469/
  5. Bobbi Campbell Diary, 1983-1984, MSS 96-33 https://calisphere.org/collections/3684/
  6. Mobilization Against AIDS Records, 1984-1995,
    MSS 95-03 https://calisphere.org/collections/14922/
  7. People vs. Owen Bathhouse Closure Litigation, 1984-1987
    (SFPL SFH 31) https://calisphere.org/collections/26990/
  8. AIDS History Project — Ephemera Collection, 1981-2002, MSS 2000-31 (posters) https://calisphere.org/collections/466/

National Endowment for the Humanities

The San Francisco Bay Area’s Response to the AIDS Epidemic has been made possible in part by a major grant from the National Endowment for the Humanities: Exploring the human endeavor.

 

New Archives Intern: Elizabeth Popiel

Today’s post is an introduction from Elizabeth Popiel, our newest intern here in the Archives who will be working on piloting and testing some of the key pieces of our digital forensics lab and workstations.


Portrait of Elizabeth Popiel.

Elizabeth Popiel

Hello out there readers! My name is Elizabeth Popiel and I’ll be interning at the UCSF Archives & Special Collections working with some of the early born-digital collections here in the Library this summer. I’m a second year graduate student in the School of Information at the University of Michigan in Ann Arbor, with a concentration in Digital Curation, Archives and Human Computer Interaction.  I’ve always the loved exploration and discovery part of any research project and I hope to do a little of that here this summer as well.

I’m enjoying being back in the Bay Area before heading back to the Midwest for my last year of school. I love road tripping along the coast and seeing everything out there from the Redwoods to the Historic Forts, museums and interesting locations. I was born in Canada and have traveled extensively from places such as Bern to Tasmania, Singapore to Beijing and back again. It’s great to get to see and learn perspectives that differ from your own and to learn to appreciate them when you approach your work, especially when trying to figure out a puzzle or sort through a collection

In my past I taught English overseas, worked in broadcasting, and I have experience working in both hardware and software in Silicon Valley. I’m an old-school gamer and I still love text-adventures, joystick-based and SCUMM Engine games. Figuring out how to make them work on newer machines is always a challenge!

I like the challenge of working in research and preservation for born-digital archival collections, and at UCSF I’m hoping to be able to gain practical experience in this area. I’ll assist in getting their Digital Forensics lab up and running for collections capture, processing, and use as well as test processing some of the collections. It’s my hope that I can better understand how to work with active collections and how digital archival models can be adapted to different and unique libraries and archives such as UCSF.

In Archives, my passion in work and learning lies in the archival challenges that lay ahead in digital curation, forensic work, and audiovisual materials. One of the reasons working with UCSF Special Collections interests me is because there are so many collection pieces that need attention in order for them to remain usable for future generations. Everything from floppy disks with key scientific notes, to spreadsheets containing experiment setup in ontological medicine, or information or email communications that represent negotiations and crucial strategies during the height of the San Francisco AIDS epidemic – these all represent important parts of the history of UCSF and its legacy and I’m excited to contribute to preserving that legacy.

Experiments with Digital Tools in the Archives — OCR

Working on digital “stuff” in the archives is always fascinating, because it blurs the borders between digital and physical. Most of the work the takes up my time is at these borders. Physical stuff requires lots of human touches to transition to “digital,” and digital stuff similarly requires lots tending by humans to ensure that it is preserved physically. After all, the 1s and 0s are stored physically somewhere, even if on the cloud or in DNA.

We’re currently working on several projects to convert physical materials to digital text. The huge quantities of rich and complicated textual material in archival collections is full of potential for use as data in both computational health research and also digital medical humanities work, but to be usable for these kinds of projects it needs to be converted to digital text or data, so that it can be interpreted by computers. To get to this point the documents must be scanned, and the scanned documents must either be transcribed, which can be immensely labor intensive, or converted directly by computers using a software that can perform Optical Character Recognition, or OCR. One of our projects using OCR to extract text from a document provides a fascinating look into the world of computer vision.

A pen and ink illustration of the lungs and a lymph gland from the Ralph Sweet Collection of Medical Illustrations

An example of the illustrations in the Ralph Sweet Collection

The Ralph Sweet Collection of Medical Illustration contains extraordinary examples of the work of one of the most renowned medical illustrators in the United States, so we’re working on digitizing the collection and putting it online. To do this we need to have detailed metadata — the kind of information you might expect to find in a catalog record, title, date, author — about each illustration. Currently this metadata for the Sweet Collection exists only in the form of printed index that was written on a typewriter. We can scan the index, but we do not have the labor to transcribe each of the 2500 or so entries. This is a job for OCR.

The image below shows what a page of the Ralph Sweet index looks like. This is the metadata that we want to be able to extract and turn into digital text so that it can be understood by a computer and used as data.

A page of an type-written index of the Ralph Sweet Collection, showing metadata about each illustration in the colleciton.

A page of the index for the Ralph Sweet Collection.

One of the first problems we encountered in attempting to extract text from this document is a classic difficulty of computer vision. As English-speaking humans, we know by looking at this document that it contains three columns, and that the order in which to read the page is top to bottom by column, starting on the left and moving right. To a computer however, it is simply a page full of text, and there is no way to know whether or not the text is broken into columns or whether each line should be read all the way across the page. This simple task presented a difficulty for most of the software that we tested, but we found one software which could identify these columns easily. The software is called Tesseract, and it was actually developed in the 1980’s but continues to be a good open-source tool to perform OCR.

If we plug the above page into Tesseract, we get mostly recognizable text, which in itself is pretty miraculous when you think about it. Looking at the text though, it quickly becomes clear that it is not an exact transcription of what’s on the page. There are misspellings (“Iivev”), and some chunks of text have been separated from the entry in which they should appear (“horizontal”).

An image of the text-output of the software tesseract showing errors in transciption.

An example of the text extracted from the Ralph Sweet Collection Index by Tesseract.

Digging into the way that Tesseract (and OCR software more generally) works can help us begin to understand why these errors are cropping up. Plus, it looks really cool.

OCR programs have to go through a set of image manipulation processes to help them decide which marks on the page are text — and hence should be interpreted — and which are other marks that can be ignored. This all happen behind the scenes, and usually this involves deciding what the background parts of the image are and blurring them out, increasing the image contrast, and making the image bi-tonal so that everything on the page is only black or white. Then, the computer can trace the black pixels on the image and get a series of shapes which it can use to begin attempting to interpret as text. The image below shows the shapes that Tesseract has identified as letters and traced out for interpretation. Each new color indicates that the computer believes it has moved on to a new letter.

A page of colorful text on a black background illustrating the text that has been automatically traced from the Ralph Sweet Index by the computer program Tessearact.

The result of Tesseract tracing the letters it has interpreted. Each new color is something that’s been identified as a new letter.

Interestingly, when comparing the computer tracing of the letters to the original image you can see that Tesseract has already made the assumption that the black spaces from the three-hole punch in the top of the page are not letters, and thus it should not bother to trace them. Lucky for us, that’s correct.

Next the computer has to take all these letters and turn them into words. In actual practice it’s not quite this simple, but basically the computer iterates on each letter identification that it believes it has made by testing whether or not that word is in its dictionary, and thus whether or not it is likely to be a word. If the combination of letters that the computer thinks it sees are not a word, then it will go back and make a new guess about the letters and test whether or not that’s a word, and so on. Part of this whole process is to chunk the letters into words using their actual spacing on the page. Below you can see an image of how Tesseract has begun to identify words using the spaces between lines and letters.

A view of a page of the Ralph Sweet Index showing each word as a blue rectangle encompassing the space taken up by that block of text against a black background -- the "word" output of the OCR program Tesseract.

The “words” that the OCR software has identified on the page. Each blue rectangle represents a space that Tesseract has marked as containing a word.

In addition to checking the word against the dictionary though, most OCR programs also use the context of the surrounding words to attempt to make a better guess about the text. In most cases this is helpful — if the computer has read a sentence that says “the plant requires wader” it should be a relatively easy task to decide that the last word is actually “water.” In our case though, this approach breaks down. The text we want the computer to extract in this case is not sentences, but rather (meta)data. The meaning of the language has little influence on how each individual word should be read. One of the next steps for us will be trying to figure out how to better instruct Tesseract about the importance of context in making word-identification decisions (i.e., that it’s not important).

Finally, as the OCR software interprets the text it also identifies blocks of words that it believes should be grouped together, like a paragraph. Below you can see the results of this process with Tesseract.

A view of the different elements of tesseract's text identification showing letters traced in primary colors and contained in yellow bounding boxes, words set against blue rectangles outlining the space they encompass, and blocks of text outlined in larger bounding boxes and numbered -- all of this set against a black background.

This view shows all of the elements of Tesseract’s word identification combined. Text has been traced in color, separate letters are contained in bounding boxes, words are contained in blue rectangles, and blocks are contained in larger bounding boxes and are numbered (though the numbers are a bit difficult to see).

A line has been drawn around each block of text, and it has been given a number indicating the order in which the computer is reading it. Here we can see the source of one of the biggest problems of the OCR-generated text from earlier. Tesseract is in-accurately excluding a lot of words from their proper blocks. In the above photo, the word “Pen” is a good example. It is a part of block 20, but it has been interpreted by the computer as it’s own block — block 21 — and has been set aside to appear in the text file after block 20. Attempting to solve this problem will be our next hurdle, and hopefully we can catch you up after we are successful.

Using OCR to extract text from digital images can be a frustrating endeavor if accuracy is a necessity, but it is also a fascinating illustration of the way computers see and make decisions. Anytime we ask computers to perform tasks that interface with humans, we will always be grappling with similar issues.