Abstract
Our project, In Codice Ratio, is an interdisciplinary research initiative for analyzing content of historical documents conserved in the Vatican Secret Archives (VSA). As most of such documents are digitized as images, Machine Transcription is both an enabler to the application of Knowledge Discovery techniques, as well as a useful tool to the paleographer for speeding up the transcription process. Our approach involves a convolutional neural network to recognize characters, statistical language models to compose and rank word transcriptions, and crowdsourcing for scalable training data collection. We have conducted experiments on pages from the medieval manuscript collection known as the Vatican Registers. Our results show that almost all the considered words can be transcribed without significant spelling errors.
This work is an extended abstract of [4].
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For such word images, most of first-ranked transcriptions have \({\le }3\) spelling errors.
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Acknowledgments
We thank NVIDIA Corporation for the donation of a Quadro M5000 GPU, and Regione Lazio (Progetti di Gruppi di Ricerca) and Roma Tre University (Piano Straordinario di Sviluppo della Ricerca di Ateneo) for supporting our project “In Codice Ratio”.
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Ammirati, S., Firmani, D., Maiorino, M., Merialdo, P., Nieddu, E. (2019). In Codice Ratio: Machine Transcription of Medieval Manuscripts. In: Manghi, P., Candela, L., Silvello, G. (eds) Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, vol 988. Springer, Cham. https://doi.org/10.1007/978-3-030-11226-4_15
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