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 141 · 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 141
Collection-level dates
1948–1977
Open PDF at page 141 ↗

Page transcription

page 8

discover all kinds of interesting things. Things that interested me perhaps at that particular time. I want to know, for example, what does Quercus alba look like? And with those two words, go and look into that tremendous collection of things and find out something that looked like Quercus alba, or a thing which people call Quercus alba. And I could discover what the thing looked like; I could discover perhaps where the plants representing Quercus alba grew, and all sorts of little goodies and information about them. Now another way of thinking about herbarium specimens is this: that they are a fine set of historical documents. Many of the early collections in this country will bear the label Boston, Massachusetts, and I suspect that if we went back to Boston, Massachusetts right now, we would discover that the place that the man picked up this particular specimen is now completely covered with concrete and steel and yet we have some evidence that this was indeed a plant which did grow in that area before man came along and messed around with the environment. So in a sense it is a, it can be used as a fine record of what we have had in the past, although it is an incomplete record. I also want to give you some feeling about these particular specimens because in your handling of them in the next few weeks, I want you to have the same sense of importance that these particular specimens have such that you will treat them with the respect to which they, you should give them. Now how do we use herbarium specimens? We use them again according to the dictates of the time in which we exist. Primarily, we derive information about the plant materials from herbarium specimens concerning their gross external morphology. But as we have gone along improving our data collection methods and our refinements in what we want to know about plants, we have been able to use these old brown herbarium sheets in many different interesting sorts of ways. For example, although the specimen be dried, it still maintains quantities of cellulosic material and allied substances which do not modify them tremendously upon drying. For example, you can study quite adequately the anatomical features of certain parts of x plants. If you are ingenuous