The theater of the Spanish Early Modern period is a textual collection composed of thousands of works and hundreds of playwrights and is one of the greatest examples of Spanish and Western literature. These works often present a wide range of textual problems stemming mainly from the copying process itself given that it may alter the meaning of the original works written by the authors. Therefore, identifying autograph testimonies written directly by the playwrights themselves are of particular importance. To perform this identification automatically, we address an approach for writer identification that combines Deep Convolutional and Recurrent Neural Networks with n-gram language models. This is posed as a classification problem, introducing a probabilistic framework that goes beyond the plain classification of recognized handwritten text. Preliminary experiments are conducted to validate our proposal to distinguish between Lope de Vega’s manuscripts and other non-Lope hands.