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This project aims at identifying all pictorial elements in educational texts from 1800-1850 to explore the interplay between progressive education and print media in the early nineteenth century. The resulting research will characterize the extent to which wood engravings and other reprographic materials were shared among educational publishers. The researcher will extract specific features from page images, such as illustration location, using advances in machine learning. The project intends to make use of the process developed to identify pictorial elements to motivate a new metadata field that describes the location and type of illustrations on the page. An ultimate goal of the project is to move toward “machine-read” texts where the data generated by classifiers and dimensionality reduction techniques are bundled as metadata with the corresponding volumes and made available to future research. (“Machine-read” is a term is borrowed from researcher Ben Schmidt.)

Project report: Derived Metadata for Early 19C Illustrations: ACS Grant Final Report

Semantic Phasor Embeddings

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