Speaker
Description
The English common law rests on a documentary record of staggering size and frustrating inaccessibility. At least eleven million pages of medieval legal manuscripts survive at the National Archives at Kew, the product of a documentary machine at Westminster and county courts that churned out records at a rate of tens of thousands per year. For comparison, roughly six to seven thousand literary manuscripts survive from the same period and place. These records are digitized and freely available online, but this has not rendered them accessible. The indices that exist are an array of finding aids and researcher-produced spreadsheets that are not connected and do not cover the full archive, and the vast majority of the manuscripts remain untranscribed. Thus, our project asks not how faithfully we might reproduce each page, but how much of this vast archive can be made navigable as quickly and simply as possible to the historians who need it. We built an open-source pipeline aimed at rough, mostly-correct transcription at scale. Trained on roughly 4,000 hand-transcribed lines, it pairs a neural line-segmentation with a convolutional–recurrent network (a CNN feeding an LSTM, decoded with CTC and a Latin n-gram language model); this brings word error down to around twelve percent and character error to around four, accurate enough to let a researcher locate, sort, and triage cases that would otherwise cost a trained paleographer some five minutes a line to decipher. We paired this with a search engine and a Claude-based assistant to sort through HTR errors and variations to return all relevant results. The tool is designed to work in tandem with an experienced reader, not to replace one; its errors are, by design, the kind a human can readily catch. This paper describes the project from the historian’s side as the practical work of turning the parchment maze of the National Archives into a searchable record of medieval life.