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Participate in enhancing access to collections covering 400 years of Virginia history, people, and culture. From peace to wartime, wedding announcements to world—changing events, court records to letters home, there is something for everyone. Help us tell the story of all Virginians—the famous, the infamous and even the anonymous—and join us in Making History.
Our crowdsourcing projects have three different types of activities. You may wish to try several different collections and activities. Depending on your skills and preferences, you may have more success with one over another. Select the one that you enjoy the most!
The Library of Virginia receives funding from the Institute of Museum and Library Services to support our crowdsourcing programs, including our transcribe-a-thons and collections available on From the Page. Please share your thoughts about the Making History project and programming in this brief survey:
Projects
Identification
This identification process involves viewing documents and selecting the matching image. For the WWII Project, this process helps to sort the different forms into separate groups for indexing to accurately gather all the information and data.
WWII Separation Notices: Army
Transcription
Transcription involves viewing a digital image of an original document and creating a typed version of all of the text. Capture all of the typos, misspelled words, and errors exactly as they appear in the original document.
St. John's Episcopal Church vestry books, 1730-1900
Virginia Untold: Petitions to Remain in the Commonwealth
Indexing
Indexing involves viewing a digital image of the original document and entering select information, such as names and dates. Rather than capturing all the text, volunteers must use their judgment to find the correct information within the document.
Virginia Untold: Free Registers
World War II Separation Notices: Army I
World War II Separation Notices: U.S. Marine Corps
Text Correction
OCR, or Optical Character Recognition, is a process by which software recognizes the shapes of letters on a newspaper page and translates them into a searchable text file. Help improve the accuracy of OCR text by comparing it against the original historical newspaper and making additions and corrections.