The task of automated recognition of handwritten texts requires various phases and technologies both optical and language related. This article describes an approach for performing this task in a comprehensive manner, using machine learning throughout all phases of the process. In addition to the explanation of the employed methodology, it describes the process of building and evaluating a model of manuscript recognition for the Spanish language. The original contribution of this article is given by the training and evaluation of Offline HTR models for Spanish language manuscripts, as well as the evaluation of a platform to perform this task in a complete way. In addition, it details the work being carried out to achieve improvements in the models obtained, and to develop new models for different complex corpora that are more difficult for the HTR task.