  Encoded (or ciphered) manuscripts are a special type of historical documents
that contain encrypted text. The automatic recognition of this kind of
documents is challenging because: 1) the cipher alphabet changes from one
document to another, 2) there is a lack of annotated corpus for training and 3)
touching symbols make the symbol segmentation difficult and complex. To
overcome these difficulties, we propose a novel method for handwritten ciphers
recognition based on few-shot object detection. Our method first detects all
symbols of a given alphabet in a line image, and then a decoding step maps the
symbol similarity scores to the final sequence of transcribed symbols. By
training on synthetic data, we show that the proposed architecture is able to
recognize handwritten ciphers with unseen alphabets. In addition, if few
labeled pages with the same alphabet are used for fine tuning, our method
surpasses existing unsupervised and supervised HTR methods for ciphers
recognition.
