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:

plant collecting and fieldwork in South America medicinal, culinary, or other traditional uses of plants chromosomes and plant classification early herbals and botanical illustrations botanical institutions exchanging plants, seeds, and specimens microscopy and photomicrography in botanical research How did botanists and gardeners introduce plants from one region to another? grafting, budding, and propagation of tropical fruit crops

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 35 · DO #4713 · 231_Rogers_Bx4FF4_r

Collection: David James Rogers (1918–2007) papers
Item/Folder: N. Y. Botanical Garden - Math & Biology, 1964
Digital Object: DO #4713, page 35
Collection-level dates: 1948–1977

Open PDF at page 35 ↗

Page transcription

-3-
PROCESSING TAXONOMIC PROBLEMS

Plants to be classified | Determined from present system
Assemble data (morphologic, genetic, ecologic, physiologic, etc. Organize for processing | Pattern analysis
Determine related and unrelated elements |
Cluster |
Organization into "formal" classification | Models to be derived from present methods
Nomenclature problems | Information retrieval method; logical coding scheme
Identification routines | Logical models
Application |
Breeding programs analysis of drug-producing plants, etc. | Evolutionary studies | Ecological studies | Information retrieval