  Recognizing a piece of writing as a poem or prose is usually easy for the
majority of people; however, only specialists can determine which meter a poem
belongs to. In this paper, we build Recurrent Neural Network (RNN) models that
can classify poems according to their meters from plain text. The input text is
encoded at the character level and directly fed to the models without feature
handcrafting. This is a step forward for machine understanding and synthesis of
languages in general, and Arabic language in particular. Among the 16 poem
meters of Arabic and the 4 meters of English the networks were able to
correctly classify poem with an overall accuracy of 96.38\% and 82.31\%
respectively. The poem datasets used to conduct this research were massive,
over 1.5 million of verses, and were crawled from different nontechnical
sources, almost Arabic and English literature sites, and in different
heterogeneous and unstructured formats. These datasets are now made publicly
available in clean, structured, and documented format for other future
research. To the best of the authors' knowledge, this research is the first to
address classifying poem meters in a machine learning approach, in general, and
in RNN featureless based approach, in particular. In addition, the dataset is
the first publicly available dataset ready for the purpose of future
computational research.
