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 97 · DO #4744 · 231_Rogers_Bx4FF35_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Museum Grant Proposal, 1969
Digital Object
DO #4744, page 97
Collection-level dates
1948–1977
Open PDF at page 97 ↗

Page transcription

Growth of the Herbarium collections is indicated below:

1948 1958 1968
Phanerogams, ferns, and vascular cryptogams 52,000 90,000 127,000
Lichens 0 27,000 50,000
Bryophytes 2,000 12,000 30,000
Fungi 0 10,000 10,000
Algae 0 5,000 5,000
Myxomycetes 0 300 300

Total 54,000 144,300 222,300

Important Collections in the Herbarium
Phanerogam, Fern, and Vascular Cryptogam Collections
Early Colorado collections (unicates) of Alice Eastwood, circa 1890.
Early historic collections of Colorado plants from the herbarium of the Academy of Natural Sciences of Philadelphia.
Francis Ramaley Herbarium
Abbé Letacq Herbarium
Delphinium Herbarium of Joseph A. Ewan (monographer of the genus)
William A. Weber Herbarium, 1936-68 (including special collections in Compositae)

Exsiccati
Plantes Pyreneennes (Zetterstedt)
Flora Jutlandica Exsiccata (Copenhagen Museum)
Plantae Vasculares Groenlandicae
Plantae Exsiccatae Polonicae
Plantae Exsiccatae Grayanae
Plantae Groenlandicae Exsiccatae
Plantae Suecicae Exsiccatae (Samuelsson)
Plantae Mexicanae (Pringle)