  Seals are small coin-shaped artifacts, mostly made of lead, held with strings
to seal letters. This work presents the first attempt towards automatic reading
of text on Byzantine seal images.Byzantine seals are generally decorated with
iconography on the obverse side and Greek text on the reverse side. Text may
include the sender's name, position in the Byzantine aristocracy, and elements
of prayers. Both text and iconography are precious literary sources that wait
to be exploited electronically, so the development of computerized systems for
interpreting seals images is of paramount importance. This work's contribution
is hence a deep, two-stages, character reading pipeline for transcribing
Byzantine seal images. A first deep convolutional neural network (CNN) detects
characters in the seal (character localization). A second convolutional network
reads the localized characters (character classification). Finally, a
diplomatic transcription of the seal is provided by post-processing the two
network outputs. We provide an experimental evaluation of each CNN in isolation
and both CNNs in combination. All performances are evaluated by
cross-validation. Character localization achieves a mean average precision
<EMAIL_ADDRESS>greater than 0.9. Classification of characters cropped from ground
truth bounding boxes achieves Top-1 accuracy greater than 0.92. End-to-end
evaluation shows the efficiency of the proposed approach when compared to the
SoTA for similar tasks.
