Show simple item record

dc.contributor.advisorBroder, Alan
dc.contributor.authorAharon, Alyssa (Ayelet)
dc.identifier.citationAharon, A. (2022, May 3). Deep Torah Learning: A Deep Learning Approach to Identifying Anomalous Letters in Torah Scrolls. Undergraduate honors thesis, Yeshiva University.en_US
dc.descriptionUndergraduate honors thesis / Opt-Outen_US
dc.description.abstractOtiyot meshunot, anomalous letters in Torah scrolls, can be useful in studying the historical evolution of Torah scrolls, as well as determining the chronological and geographical origins of a particular scroll. Currently, otiyot meshunot are discovered by researchers manually examining each letter in a scroll. In order to improve the efficiency of this process, a deep learning model was built to distinguish anomalous Torah letters from normal ones. The model, trained on images of Torah scrolls, used a neural network to accomplish optical character recognition of typical Torah letters and anomaly detection of unique letters. The model succeeded in identifying numerous otiyot meshunot, and serves as a prototype for a potential product to be used in the realm of Torah research.en_US
dc.description.sponsorshipThe S. Daniel Abraham Honors Programen_US
dc.publisherYeshiva Universityen_US
dc.relation.ispartofseriesS. Daniel Abraham Honors Student Theses;May 3, 2022
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectOtiyot meshunoten_US
dc.subjectTorah studyen_US
dc.subjectTorah researchen_US
dc.subjectoptical character recognition (OCR)en_US
dc.subjectanomaly detectionen_US
dc.titleDeep Torah Learning: A Deep Learning Approach to Identifying Anomalous Letters in Torah Scrollsen_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States