Hunt Institute for Botanical Documentation
A Research Division of Carnegie Mellon University

Hunt Institute Archives Text Discovery Platform

Search a large and growing portion of our online collections, including handwritten documents.
PROTOTYPE

This prototype uses state-of-the-art artificial intelligence, including a vision-language model (VLM) capable of reading handwritten documents as well as typed and printed text, to create searchable transcriptions of digitized materials in the Hunt Institute Archives. This makes it possible to search the textual contents of individual pages, including material that may not be described in the archival catalog.

Use Keyword search for specific words, names, dates, scientific names, or phrases. Try Semantic search (experimental) to describe a topic, question, or kind of material when you do not know the exact wording used in the documents.

About the AI-generated transcriptions

The transcriptions are generated automatically from page images and may contain errors, especially with difficult handwriting, unusual names, multiple languages, image-quality problems, or complex layouts. They are intended primarily as a discovery aid rather than authoritative transcriptions.

Each result provides the generated transcription and links to the original digitized material and associated archival description so that readings can be checked against the source. The transcription workflow uses AI models run locally by the Hunt Institute.

About Keyword and Semantic search

Keyword search is the default and matches the wording in the transcriptions. Results contain all your terms. Use quotes for an exact phrase. Substring matching is supported, so aceae can find plant-family names ending in -aceae.

Semantic search (experimental) is useful when you know what kind of material you are looking for but do not know the words used in the documents. It ranks transcribed passages by similarity of meaning, so relevant results may not contain the exact words in your query.

Semantic queries can be broad research topics, descriptions of activities or relationships, or natural-language questions. For example:

Semantic search is not a chatbot: a question is used as a search query, and the system returns archival passages that appear conceptually related to it rather than generating an answer or summary. Short descriptions and ordinary research questions generally work better than lists of disconnected keywords. Quotation marks have no special meaning in Semantic mode. Cross-language matching may work in some cases, but it should not be treated as translation.

Keyword and Semantic search are complementary. Keyword search lets you require particular wording; Semantic search can surface differently worded passages about the same subject. Depending on the research question, trying both can reveal different useful material.

Open a result: use the prominent page-and-transcription link to see the metadata, PDF, and full transcription. Keyword-search terms are highlighted in the transcription.

Archives Collections Database (ArchivesSpace): the Collection, Item/Folder, and Digital Object links open the corresponding archival records. Collection-level dates describe the collection as a whole, not necessarily the specific item or page.

If a PDF does not load: on the detail page, use the Digital Object link, click “Go to file” in ArchivesSpace, and navigate to the page number shown here.

Current limitations
  • Automated transcriptions can contain missing or incorrect text or unintended repetition. Difficult handwriting, image quality, unusual layouts, and multiple languages can reduce accuracy. Always consult the original page image when an exact reading matters.
  • Semantic search remains experimental. Its rankings are an additional discovery aid, not a complete or definitive set of relevant results, and highly ranked pages can sometimes be only broadly related.
  • This is an active prototype. Search coverage, transcriptions, functionality, and the interface may continue to change as additional archival material is processed and the system is improved.

← Back to results

Page 127 · DO #4666 · 231_Rogers_Bx2FF27_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Taximetrics Course - Student Reports, 1966–1968
Digital Object
DO #4666, page 127
Collection-level dates
1948–1977
Open PDF at page 127 ↗

Page transcription

page 7

information for particular objects under study. (Dave) In order to allow us
to proceed with some work that I want you all to do individually, I have to
give you some basic thoughts and processes on herbarium materials. The col-
lections of materials kept by botanists in pigeonholes in cases similar to
this are the same thing as a zoologist calls museum specimens, or in our case
we call them herbarium specimens. In the last few years or a matter of fact for
a very long time, there has been a rather derogatory opinion of anyone who
meddled around with scrappy little hunks of junk, squashed out flat, dried and
turned brown and plastered down to a sheet of material of paper. I think
perhaps that I would like give a little bit of different emphasis on this
process here for benefit for those of you who have heard these arguments
have concurred with those who said we ought to stick a match to every herbarium
in the world and burn the xmd dang thing down. We have to recall that there iss
a long history in botany and that as our information about plants grew we
changed our concepts about we needed to do at any particular stage of the game.
We haven't granted been too precise in definition of what an herbarium is
specimen is supposed to be and do for us. But again, at any era at any partic-
ular time these types of things have changed. What do we have to do with it?
Personally, I am one of the greatest defenders of a collection of dried herbar-
ium materials. I think this is one of the most marvelous scientific pieces of
documents that has ever been invented. Having come from an institution where
they figured that ht they had about three million separate sheets of herbarium
material, I was always very fascinated that if I need two names, the genus and
the species name, that I could within 5 minutes go to a particular spot in
that collection of three million specimens and discover an object, a particularr
object out of all that three million and with that very rapid way of getting to
a particular piece of information out of this mass of information, I could