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 55 · DO #4926 · 231_Rogers_Bx9FF8_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Manihot - Correspondence; Publication, 1972–1973
Digital Object
DO #4926, page 55
Collection-level dates
1948–1977
Open PDF at page 55 ↗

Page transcription

two branched (92), but commonly with one or two branches (excluding any branches at the top) (79), less frequently unbranched or a single branch at top (57).

Leaves with basic number of lobes odd, but occasionally with an even number of lobes; most frequently 7- or 8-lobed (148), sometimes 9- or 10-lobed (42), occasionally 5- or 6-lobed (23), rarely 3- or 4-lobed (4) or variable, 3-, 4- and 5-lobed (9) or 4-, 5- or 6-lobed (2). Leaf-lobe shape prevalently obovate (193) but sometimes linear (35). Leaf margins of obovate leaves usually simple (176), sometimes sinuous (15) and rarely pandurate (2); of linear-shaped lobes, margins commonly sinuous (26), occasionally simple (8), rarely pandurate (1). Length of median lobe most frequently long, greater than 17 cm (105), often of moderate length, 14 to 17 cm (86) and occasionally short, less than 14 cm (37). Width of median lobe predominantly moderate, 2.6 cm to 4.8 cm (160), sometimes narrow, 1.5 cm to 2.4 cm (36), or wide, 5 cm or more (32). Petioles mostly green (115), frequently red (73), occasionally reddish green (29), seldom greenish red (11). Young foliage most frequently green (142), often reddish blue (57), sometimes bluish green (29).

AIDS TO IDENTIFY AN UNKNOWN CULTIVAR

Fig. 3 gives the major categories we have used in the classification of the cultivars. Since this classification shows the relationships of the cultivars for their gross morphological characters, it is not always a simple matter to identify an individual cultivar. To use this classification, we suggest that the investigator describe the cultivar on the basis of as many of the characters given in Table 1 as are available. Having described the plant with the characters given, proceed to Fig. 3, which gives the major categories. Following discovery of the major subdivision to which a cultivar belongs (i.e., rough-rooted, obovate lobed leaves), one then recognizes that the unknown plant belongs to one of twelve groups (Fig. 3). Within the groups