Undergraduate honors thesis / Opt-Out
Otiyot 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.
Aharon, A. (2022, May 3). Deep Torah Learning: A Deep Learning Approach to Identifying Anomalous Letters in Torah Scrolls. Undergraduate honors thesis, Yeshiva University.
*This is constructed from limited available data and may be imprecise.