Handwritten Text Recognition (HTR) in low-resource scenarios (i.e. when the amount of labeled data is scarce) is a challenging problem. This is particularly true for historical encrypted manuscripts, commonly known as ciphers, which contain secret messages and were typically used in military or diplomatic correspondence, records of secret societies, or private letters. To hide their contents, the sender and receiver created their own secret method of writing. The cipher alphabets often include digits, Latin or Greek letters, Zodiac and alchemical signs, combined with various diacritics, as well as invented ones. The first step in the decryption process is the transcription of these manuscripts, which is difficult due to the great variation in handwriting styles and cipher alphabets with a limited number of pages. Although different strategies can be considered to deal with the insufficient amount of training data (e.g., few-shot learning, self-supervised learning), the performance of available HTR models is not yet satisfactory. Thus, the proposed competition, which includes ciphers with a large number of symbol sets and scribes, aims to boost research in HTR in low-resource scenarios.