From UConn Today:
The UConn Library and the School of Engineering are working to develop new technology that applies machine learning to handwriting text recognition that will allow researchers to have improved access to handwritten historic documents.
Handwritten documents are essential for researchers, but are often inaccessible because they are unable to be searched even after they are digitized. The Connecticut Digital Archive, a project of the UConn Library, is working to change that with a $24,277 grant awarded through the Catalyst Fund of LYRASIS, a nonprofit organization that supports access to academic, scientific, and cultural heritage.
Archives and special collections from across Connecticut fill the Connecticut Digital Archive (CTDA), providing online access to a treasure of historic materials. However, the irregularity in the handwriting in many of the manuscripts leaves the historical information in these documents inaccessible to Optical Character Recognition (OCR), a transfer method used for more than 20 years to assist in document discoverability.
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See Also: LYRASIS Announces the 2020 Catalyst Fund Recipients and Their Projects
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