  In this work we propose an OCR scheme for manuscripts printed in Rashi font
that is an ancient Hebrew font and corresponding dialect used in religious
Jewish literature, for more than 600 years. The proposed scheme utilizes a
convolution neural network (CNN) for visual inference and Long-Short Term
Memory (LSTM) to learn the Rashi scripts dialect. In particular, we derive an
AutoML scheme to optimize the CNN architecture, and a book-specific CNN
training to improve the OCR accuracy. The proposed scheme achieved an accuracy
of more than 99.8% using a dataset of more than 3M annotated letters from the
Responsa Project dataset.
